{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/representation-learning/papers/ran/3","list_of":"/task/representation-learning","task":"Representation Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":3,"pages_in_order":15,"rows_per_page":100,"rows":[201,300],"of":1439,"counts":{"archive_papers_tagged":10580,"with_a_code_link":4662,"where_syntology_ran_a_sample":1439,"not_listed_spam_title":0,"listed":10580,"listed_where_code_ran":1439,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1228,"every_run_a_failure_of_syntologys_instrument":211,"listed_with_a_run_with_no_instrument_failure":1228,"listed_every_run_a_failure_of_syntologys_instrument":211,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/representation-learning/papers/ran/1","prev":"/task/representation-learning/papers/ran/2","next":"/task/representation-learning/papers/ran/4","papers":[{"url":"/paper/pre-trained-text-to-image-diffusion-models","slug":"pre-trained-text-to-image-diffusion-models","title":"Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control","date":"2024-05-09","arxiv_id":"2405.05852","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pre-trained-text-to-image-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2405.05852","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.05852"}},"official":{"repos":["ykarmesh/stable-control-representations"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/wiser-weak-supervision-and-supervised","slug":"wiser-weak-supervision-and-supervised","title":"WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer","date":"2024-05-07","arxiv_id":"2405.04078","repositories_listed":1,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":13,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/wiser-weak-supervision-and-supervised#ran","syntology_url":"https://syntology.ai/paper/2405.04078","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.04078"}},"official":{"repos":["kyrs/wiser"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/the-curse-of-diversity-in-ensemble-based","slug":"the-curse-of-diversity-in-ensemble-based","title":"The Curse of Diversity in Ensemble-Based Exploration","date":"2024-05-07","arxiv_id":"2405.04342","repositories_listed":2,"syntology":{"n":16,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":13,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/the-curse-of-diversity-in-ensemble-based#ran","syntology_url":"https://syntology.ai/paper/2405.04342","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.04342"}},"official":{"repos":["zhixuan-lin/ensemble-rl-continuous","zhixuan-lin/ensemble-rl-discrete"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":13,"ran_from_kinds":["official"]}}},{"url":"/paper/representation-learning-of-daily-movement","slug":"representation-learning-of-daily-movement","title":"Representation Learning of Daily Movement Data Using Text Encoders","date":"2024-05-07","arxiv_id":"2405.04494","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/representation-learning-of-daily-movement#ran","syntology_url":"https://syntology.ai/paper/2405.04494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.04494"}},"official":{"repos":["alexcapstick/text-encoders-for-daily-movement-data"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/understanding-multimodal-contrastive-learning-1","slug":"understanding-multimodal-contrastive-learning-1","title":"Weighted Point Cloud Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric","date":"2024-04-30","arxiv_id":"2404.19228","repositories_listed":0,"syntology":{"n":7,"n_ran":5,"n_constructed":4,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/understanding-multimodal-contrastive-learning-1#ran","syntology_url":"https://syntology.ai/paper/2404.19228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.19228"}},"official":null}},{"url":"/paper/unifs-universal-few-shot-instance-perception","slug":"unifs-universal-few-shot-instance-perception","title":"UniFS: Universal Few-shot Instance Perception with Point Representations","date":"2024-04-30","arxiv_id":"2404.19401","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unifs-universal-few-shot-instance-perception#ran","syntology_url":"https://syntology.ai/paper/2404.19401","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.19401"}},"official":{"repos":["jin-s13/unifs"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/temporal-graph-odes-for-irregularly-sampled","slug":"temporal-graph-odes-for-irregularly-sampled","title":"Temporal Graph ODEs for Irregularly-Sampled Time Series","date":"2024-04-30","arxiv_id":"2404.19508","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/temporal-graph-odes-for-irregularly-sampled#ran","syntology_url":"https://syntology.ai/paper/2404.19508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.19508"}},"official":{"repos":["gravins/tg-ode"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/conpro-learning-severity-representation-for","slug":"conpro-learning-severity-representation-for","title":"ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization","date":"2024-04-29","arxiv_id":"2404.18831","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/conpro-learning-severity-representation-for#ran","syntology_url":"https://syntology.ai/paper/2404.18831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.18831"}},"official":{"repos":["hong7cong/conpro"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-diffusion-autoencoders-toward","slug":"causal-diffusion-autoencoders-toward","title":"Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models","date":"2024-04-27","arxiv_id":"2404.17735","repositories_listed":1,"syntology":{"n":17,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":5,"n_honours":3,"n_violates":0,"n_no_contract":6,"n_pointer_only":17,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 3 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/causal-diffusion-autoencoders-toward#ran","syntology_url":"https://syntology.ai/paper/2404.17735","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.17735"}},"official":{"repos":["akomand/causaldiffae"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/bounding-the-expected-robustness-of-graph","slug":"bounding-the-expected-robustness-of-graph","title":"Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks","date":"2024-04-27","arxiv_id":"2404.17947","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/bounding-the-expected-robustness-of-graph#ran","syntology_url":"https://syntology.ai/paper/2404.17947","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.17947"}},"official":{"repos":["sennadir/gcorn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unleashing-the-potential-of-fractional","slug":"unleashing-the-potential-of-fractional","title":"Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND","date":"2024-04-26","arxiv_id":"2404.17099","repositories_listed":2,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unleashing-the-potential-of-fractional#ran","syntology_url":"https://syntology.ai/paper/2404.17099","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.17099"}},"official":{"repos":["zknus/iclr2024-frond","zknus/torchfde"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/opendlign-enhancing-open-world-3d-learning","slug":"opendlign-enhancing-open-world-3d-learning","title":"OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images","date":"2024-04-25","arxiv_id":"2404.16538","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/opendlign-enhancing-open-world-3d-learning#ran","syntology_url":"https://syntology.ai/paper/2404.16538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.16538"}},"official":{"repos":["Yebulabula/OpenDlign"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-regression-representation-learning-with","slug":"deep-regression-representation-learning-with","title":"Deep Regression Representation Learning with Topology","date":"2024-04-22","arxiv_id":"2404.13904","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-regression-representation-learning-with#ran","syntology_url":"https://syntology.ai/paper/2404.13904","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.13904"}},"official":{"repos":["needylove/ph-reg"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/vim4path-self-supervised-vision-mamba-for","slug":"vim4path-self-supervised-vision-mamba-for","title":"Vim4Path: Self-Supervised Vision Mamba for Histopathology Images","date":"2024-04-20","arxiv_id":"2404.13222","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vim4path-self-supervised-vision-mamba-for#ran","syntology_url":"https://syntology.ai/paper/2404.13222","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.13222"}},"official":{"repos":["atlasanalyticslab/vim4path"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/finerec-exploring-fine-grained-sequential","slug":"finerec-exploring-fine-grained-sequential","title":"FineRec:Exploring Fine-grained Sequential Recommendation","date":"2024-04-19","arxiv_id":"2404.12975","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/finerec-exploring-fine-grained-sequential#ran","syntology_url":"https://syntology.ai/paper/2404.12975","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.12975"}},"official":{"repos":["zhang-xiaokun/finerec"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/hypergraph-self-supervised-learning-with","slug":"hypergraph-self-supervised-learning-with","title":"Hypergraph Self-supervised Learning with Sampling-efficient Signals","date":"2024-04-18","arxiv_id":"2404.11825","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/hypergraph-self-supervised-learning-with#ran","syntology_url":"https://syntology.ai/paper/2404.11825","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.11825"}},"official":{"repos":["coco-hut/se-hssl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/dacad-domain-adaptation-contrastive-learning","slug":"dacad-domain-adaptation-contrastive-learning","title":"DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series","date":"2024-04-17","arxiv_id":"2404.11269","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dacad-domain-adaptation-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2404.11269","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.11269"}},"official":{"repos":["zamanzadeh/DACAD"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tripod-three-complementary-inductive-biases","slug":"tripod-three-complementary-inductive-biases","title":"Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning","date":"2024-04-16","arxiv_id":"2404.10282","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/tripod-three-complementary-inductive-biases#ran","syntology_url":"https://syntology.ai/paper/2404.10282","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10282"}},"official":{"repos":["kylehkhsu/tripod"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cluster-based-graph-collaborative-filtering","slug":"cluster-based-graph-collaborative-filtering","title":"Cluster-based Graph Collaborative Filtering","date":"2024-04-16","arxiv_id":"2404.10321","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cluster-based-graph-collaborative-filtering#ran","syntology_url":"https://syntology.ai/paper/2404.10321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10321"}},"official":{"repos":["zhao254014/clustergcf"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neuro-inspired-information-theoretic","slug":"neuro-inspired-information-theoretic","title":"Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning","date":"2024-04-15","arxiv_id":"2404.09403","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/neuro-inspired-information-theoretic#ran","syntology_url":"https://syntology.ai/paper/2404.09403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.09403"}},"official":{"repos":["joshuaxiao98/ithp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tslanet-rethinking-transformers-for-time","slug":"tslanet-rethinking-transformers-for-time","title":"TSLANet: Rethinking Transformers for Time Series Representation Learning","date":"2024-04-12","arxiv_id":"2404.08472","repositories_listed":2,"syntology":{"n":16,"n_ran":13,"n_constructed":8,"n_ran_checked":8,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"13 ran (of which 8 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tslanet-rethinking-transformers-for-time#ran","syntology_url":"https://syntology.ai/paper/2404.08472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.08472"}},"official":{"repos":["emadeldeen24/tslanet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/mindbridge-a-cross-subject-brain-decoding","slug":"mindbridge-a-cross-subject-brain-decoding","title":"MindBridge: A Cross-Subject Brain Decoding Framework","date":"2024-04-11","arxiv_id":"2404.07850","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":3,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mindbridge-a-cross-subject-brain-decoding#ran","syntology_url":"https://syntology.ai/paper/2404.07850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07850"}},"official":{"repos":["littlepure2333/mindbridge"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/two-effects-one-trigger-on-the-modality-gap","slug":"two-effects-one-trigger-on-the-modality-gap","title":"Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models","date":"2024-04-11","arxiv_id":"2404.07983","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/two-effects-one-trigger-on-the-modality-gap#ran","syntology_url":"https://syntology.ai/paper/2404.07983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07983"}},"official":{"repos":["lmb-freiburg/two-effects-one-trigger"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unified-language-driven-zero-shot-domain","slug":"unified-language-driven-zero-shot-domain","title":"Unified Language-driven Zero-shot Domain Adaptation","date":"2024-04-10","arxiv_id":"2404.07155","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unified-language-driven-zero-shot-domain#ran","syntology_url":"https://syntology.ai/paper/2404.07155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07155"}},"official":{"repos":["Yangsenqiao/ULDA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/frequency-decomposition-driven-unsupervised","slug":"frequency-decomposition-driven-unsupervised","title":"Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation","date":"2024-04-06","arxiv_id":"2404.04531","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/frequency-decomposition-driven-unsupervised#ran","syntology_url":"https://syntology.ai/paper/2404.04531","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04531"}},"official":{"repos":["sstary/ssrs"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/soft-prompting-with-graph-of-thought-for","slug":"soft-prompting-with-graph-of-thought-for","title":"Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning","date":"2024-04-06","arxiv_id":"2404.04538","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/soft-prompting-with-graph-of-thought-for#ran","syntology_url":"https://syntology.ai/paper/2404.04538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04538"}},"official":{"repos":["shishicode/agot"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/on-exploring-pde-modeling-for-point-cloud","slug":"on-exploring-pde-modeling-for-point-cloud","title":"On Exploring PDE Modeling for Point Cloud Video Representation Learning","date":"2024-04-06","arxiv_id":"2404.04720","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-exploring-pde-modeling-for-point-cloud#ran","syntology_url":"https://syntology.ai/paper/2404.04720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04720"}},"official":{"repos":["zhh6425/MotionPointNet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dwell-in-the-beginning-how-language-models","slug":"dwell-in-the-beginning-how-language-models","title":"Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval","date":"2024-04-05","arxiv_id":"2404.04163","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":10,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dwell-in-the-beginning-how-language-models#ran","syntology_url":"https://syntology.ai/paper/2404.04163","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04163"}},"official":{"repos":["cxcscmu/longembeddinganalysis"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unveiling-llms-the-evolution-of-latent","slug":"unveiling-llms-the-evolution-of-latent","title":"Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph","date":"2024-04-04","arxiv_id":"2404.03623","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unveiling-llms-the-evolution-of-latent#ran","syntology_url":"https://syntology.ai/paper/2404.03623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03623"}},"official":{"repos":["Ipazia-AI/latent-explorer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bishop-bi-directional-cellular-learning-for","slug":"bishop-bi-directional-cellular-learning-for","title":"BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model","date":"2024-04-04","arxiv_id":"2404.03830","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bishop-bi-directional-cellular-learning-for#ran","syntology_url":"https://syntology.ai/paper/2404.03830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03830"}},"official":{"repos":["magics-lab/bishop"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/masked-completion-via-structured-diffusion","slug":"masked-completion-via-structured-diffusion","title":"Masked Completion via Structured Diffusion with White-Box Transformers","date":"2024-04-03","arxiv_id":"2404.02446","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/masked-completion-via-structured-diffusion#ran","syntology_url":"https://syntology.ai/paper/2404.02446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02446"}},"official":{"repos":["ma-lab-berkeley/crate"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/propensity-score-alignment-of-unpaired","slug":"propensity-score-alignment-of-unpaired","title":"Propensity Score Alignment of Unpaired Multimodal Data","date":"2024-04-02","arxiv_id":"2404.01595","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":5,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","sample_list":"/paper/propensity-score-alignment-of-unpaired#ran","syntology_url":"https://syntology.ai/paper/2404.01595","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01595"}},"official":{"repos":["valence-labs/prop-score-pairing"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/iisan-efficiently-adapting-multimodal","slug":"iisan-efficiently-adapting-multimodal","title":"IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT","date":"2024-04-02","arxiv_id":"2404.02059","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/iisan-efficiently-adapting-multimodal#ran","syntology_url":"https://syntology.ai/paper/2404.02059","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02059"}},"official":{"repos":["gair-lab/iisan","jjgenailab/iisan"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/nerf-mae-masked-autoencoders-for-self","slug":"nerf-mae-masked-autoencoders-for-self","title":"NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields","date":"2024-04-01","arxiv_id":"2404.01300","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/nerf-mae-masked-autoencoders-for-self#ran","syntology_url":"https://syntology.ai/paper/2404.01300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01300"}},"official":{"repos":["zubair-irshad/NeRF-MAE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hypeboy-generative-self-supervised","slug":"hypeboy-generative-self-supervised","title":"HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs","date":"2024-03-31","arxiv_id":"2404.00638","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hypeboy-generative-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2404.00638","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00638"}},"official":{"repos":["kswoo97/hypeboy"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/addressing-loss-of-plasticity-and","slug":"addressing-loss-of-plasticity-and","title":"Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning","date":"2024-03-31","arxiv_id":"2404.00781","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/addressing-loss-of-plasticity-and#ran","syntology_url":"https://syntology.ai/paper/2404.00781","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00781"}},"official":{"repos":["mohmdelsayed/upgd"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-embeddings-the-promise-of-visual-table","slug":"beyond-embeddings-the-promise-of-visual-table","title":"Beyond Embeddings: The Promise of Visual Table in Visual Reasoning","date":"2024-03-27","arxiv_id":"2403.18252","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":3,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 2 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/beyond-embeddings-the-promise-of-visual-table#ran","syntology_url":"https://syntology.ai/paper/2403.18252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18252"}},"official":{"repos":["lavi-lab/visual-table"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hill-hierarchy-aware-information-lossless","slug":"hill-hierarchy-aware-information-lossless","title":"HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification","date":"2024-03-26","arxiv_id":"2403.17307","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hill-hierarchy-aware-information-lossless#ran","syntology_url":"https://syntology.ai/paper/2403.17307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17307"}},"official":{"repos":["rooooyy/hill"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-clustering-based-visual-representation","slug":"neural-clustering-based-visual-representation","title":"Neural Clustering based Visual Representation Learning","date":"2024-03-26","arxiv_id":"2403.17409","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/neural-clustering-based-visual-representation#ran","syntology_url":"https://syntology.ai/paper/2403.17409","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17409"}},"official":{"repos":["guikunchen/fec"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/gtc-gnn-transformer-co-contrastive-learning","slug":"gtc-gnn-transformer-co-contrastive-learning","title":"GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation","date":"2024-03-22","arxiv_id":"2403.15520","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/gtc-gnn-transformer-co-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2403.15520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.15520"}},"official":{"repos":["phd-lanyu/gtc"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-balancing-representation-learning","slug":"contrastive-balancing-representation-learning","title":"Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation","date":"2024-03-21","arxiv_id":"2403.14232","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":4,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"7 ran (of which 4 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/contrastive-balancing-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2403.14232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14232"}},"official":{"repos":["euzmin/Contrastive-Balancing-Representation-Network-CRNet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":4,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/eye-gaze-guided-multi-modal-alignment","slug":"eye-gaze-guided-multi-modal-alignment","title":"Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning","date":"2024-03-19","arxiv_id":"2403.12416","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/eye-gaze-guided-multi-modal-alignment#ran","syntology_url":"https://syntology.ai/paper/2403.12416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12416"}},"official":{"repos":["momarky/egma"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/do-generated-data-always-help-contrastive","slug":"do-generated-data-always-help-contrastive","title":"Do Generated Data Always Help Contrastive Learning?","date":"2024-03-19","arxiv_id":"2403.12448","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/do-generated-data-always-help-contrastive#ran","syntology_url":"https://syntology.ai/paper/2403.12448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12448"}},"official":{"repos":["pku-ml/adainf"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/flowerformer-empowering-neural-architecture","slug":"flowerformer-empowering-neural-architecture","title":"FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer","date":"2024-03-19","arxiv_id":"2403.12821","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/flowerformer-empowering-neural-architecture#ran","syntology_url":"https://syntology.ai/paper/2403.12821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12821"}},"official":{"repos":["y0ngjaenius/cvpr2024_flowerformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/hvdistill-transferring-knowledge-from-images","slug":"hvdistill-transferring-knowledge-from-images","title":"HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation","date":"2024-03-18","arxiv_id":"2403.11817","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hvdistill-transferring-knowledge-from-images#ran","syntology_url":"https://syntology.ai/paper/2403.11817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11817"}},"official":{"repos":["zhangsha1024/HVDistill"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-useful-representations-of-recurrent","slug":"learning-useful-representations-of-recurrent","title":"Learning Useful Representations of Recurrent Neural Network Weight Matrices","date":"2024-03-18","arxiv_id":"2403.11998","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-useful-representations-of-recurrent#ran","syntology_url":"https://syntology.ai/paper/2403.11998","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11998"}},"official":{"repos":["vincentherrmann/rnn-weights-representation-learning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-multi-view-representation-learning","slug":"rethinking-multi-view-representation-learning","title":"Rethinking Multi-view Representation Learning via Distilled Disentangling","date":"2024-03-16","arxiv_id":"2403.10897","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":7,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"8 ran (of which 7 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/rethinking-multi-view-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2403.10897","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10897"}},"official":{"repos":["guanzhou-ke/mrdd"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/entity-alignment-with-unlabeled-dangling","slug":"entity-alignment-with-unlabeled-dangling","title":"Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases","date":"2024-03-16","arxiv_id":"2403.10978","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/entity-alignment-with-unlabeled-dangling#ran","syntology_url":"https://syntology.ai/paper/2403.10978","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10978"}},"official":{"repos":["Handon112358/NeurIPS_2024_Learning-Matchable-Prior-For-Entity-Alignment-with-Unlabeled-Dangling-Cases"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/t4p-test-time-training-of-trajectory","slug":"t4p-test-time-training-of-trajectory","title":"T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory","date":"2024-03-15","arxiv_id":"2403.10052","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/t4p-test-time-training-of-trajectory#ran","syntology_url":"https://syntology.ai/paper/2403.10052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10052"}},"official":{"repos":["daeheepark/t4p"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/coreecho-continuous-representation-learning","slug":"coreecho-continuous-representation-learning","title":"CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis","date":"2024-03-15","arxiv_id":"2403.10164","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/coreecho-continuous-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2403.10164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10164"}},"official":{"repos":["biomedia-mbzuai/coreecho"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hyper-cl-conditioning-sentence","slug":"hyper-cl-conditioning-sentence","title":"Hyper-CL: Conditioning Sentence Representations with Hypernetworks","date":"2024-03-14","arxiv_id":"2403.09490","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hyper-cl-conditioning-sentence#ran","syntology_url":"https://syntology.ai/paper/2403.09490","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09490"}},"official":{"repos":["hyu-nlp/hyper-cl"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/equiav-leveraging-equivariance-for-audio","slug":"equiav-leveraging-equivariance-for-audio","title":"EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning","date":"2024-03-14","arxiv_id":"2403.09502","repositories_listed":1,"syntology":{"n":13,"n_ran":6,"n_constructed":4,"n_ran_checked":6,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/equiav-leveraging-equivariance-for-audio#ran","syntology_url":"https://syntology.ai/paper/2403.09502","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09502"}},"official":{"repos":["jongsuk1/equiav"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-learning-for-time-series-1","slug":"self-supervised-learning-for-time-series-1","title":"Self-Supervised Learning for Time Series: Contrastive or Generative?","date":"2024-03-14","arxiv_id":"2403.09809","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-learning-for-time-series-1#ran","syntology_url":"https://syntology.ai/paper/2403.09809","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09809"}},"official":{"repos":["dl4mhealth/ssl_comparison"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-sparsity-principle-for-partially-observable","slug":"a-sparsity-principle-for-partially-observable","title":"A Sparsity Principle for Partially Observable Causal Representation Learning","date":"2024-03-13","arxiv_id":"2403.08335","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-sparsity-principle-for-partially-observable#ran","syntology_url":"https://syntology.ai/paper/2403.08335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.08335"}},"official":{"repos":["danrux/sparsity-crl"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/frequency-decoupling-for-motion-magnification","slug":"frequency-decoupling-for-motion-magnification","title":"Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture","date":"2024-03-12","arxiv_id":"2403.07347","repositories_listed":2,"syntology":{"n":14,"n_ran":8,"n_constructed":7,"n_ran_checked":8,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 7 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/frequency-decoupling-for-motion-magnification#ran","syntology_url":"https://syntology.ai/paper/2403.07347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07347"}},"official":{"repos":["jiafei127/fd4mm"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":7,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-graph-representation-with-knowledge","slug":"dynamic-graph-representation-with-knowledge","title":"Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis","date":"2024-03-12","arxiv_id":"2403.07719","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dynamic-graph-representation-with-knowledge#ran","syntology_url":"https://syntology.ai/paper/2403.07719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07719"}},"official":{"repos":["wonderlandxd/wikg"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/zero-shot-ecg-classification-with-multimodal","slug":"zero-shot-ecg-classification-with-multimodal","title":"Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement","date":"2024-03-11","arxiv_id":"2403.06659","repositories_listed":2,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/zero-shot-ecg-classification-with-multimodal#ran","syntology_url":"https://syntology.ai/paper/2403.06659","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06659"}},"official":{"repos":["cheliu-computation/merl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/pepsi-pathology-enhanced-pulse-sequence","slug":"pepsi-pathology-enhanced-pulse-sequence","title":"PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI","date":"2024-03-10","arxiv_id":"2403.06227","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/pepsi-pathology-enhanced-pulse-sequence#ran","syntology_url":"https://syntology.ai/paper/2403.06227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06227"}},"official":{"repos":["peirong26/PEPSI"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unity-by-diversity-improved-representation","slug":"unity-by-diversity-improved-representation","title":"Unity by Diversity: Improved Representation Learning in Multimodal VAEs","date":"2024-03-08","arxiv_id":"2403.05300","repositories_listed":4,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":4,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unity-by-diversity-improved-representation#ran","syntology_url":"https://syntology.ai/paper/2403.05300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.05300"}},"official":{"repos":["thomassutter/mmvampvae","thomassutter/mmvmvae","yangmeng96/mmvmvae-hippocampal","agostini335/mmvmvae-mimic"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["community","found_in_text","official"]}}},{"url":"/paper/contrastive-continual-learning-with","slug":"contrastive-continual-learning-with","title":"Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation","date":"2024-03-07","arxiv_id":"2403.04599","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/contrastive-continual-learning-with#ran","syntology_url":"https://syntology.ai/paper/2403.04599","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.04599"}},"official":{"repos":["lijy373/cclis"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/differentially-private-representation","slug":"differentially-private-representation","title":"Differentially Private Representation Learning via Image Captioning","date":"2024-03-04","arxiv_id":"2403.02506","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/differentially-private-representation#ran","syntology_url":"https://syntology.ai/paper/2403.02506","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.02506"}},"official":{"repos":["facebookresearch/dpcap"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/decoupling-weighing-and-selecting-for","slug":"decoupling-weighing-and-selecting-for","title":"Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks","date":"2024-03-03","arxiv_id":"2403.01400","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/decoupling-weighing-and-selecting-for#ran","syntology_url":"https://syntology.ai/paper/2403.01400","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01400"}},"official":{"repos":["tianyufan0504/was"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/eagle-eigen-aggregation-learning-for-object","slug":"eagle-eigen-aggregation-learning-for-object","title":"EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation","date":"2024-03-03","arxiv_id":"2403.01482","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":0,"n_ran_checked":12,"n_instrument":2,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":10,"n_pointer_only":5,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/eagle-eigen-aggregation-learning-for-object#ran","syntology_url":"https://syntology.ai/paper/2403.01482","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01482"}},"official":{"repos":["MICV-yonsei/EAGLE"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-disentanglement-in-downstream","slug":"revisiting-disentanglement-in-downstream","title":"Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning","date":"2024-03-01","arxiv_id":"2403.00352","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/revisiting-disentanglement-in-downstream#ran","syntology_url":"https://syntology.ai/paper/2403.00352","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.00352"}},"official":{"repos":["richard-coder-nai/disentanglement-lib-necessity"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cricavpr-cross-image-correlation-aware","slug":"cricavpr-cross-image-correlation-aware","title":"CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition","date":"2024-02-29","arxiv_id":"2402.19231","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/cricavpr-cross-image-correlation-aware#ran","syntology_url":"https://syntology.ai/paper/2402.19231","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.19231"}},"official":{"repos":["lu-feng/cricavpr"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/generalizable-whole-slide-image","slug":"generalizable-whole-slide-image","title":"Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction","date":"2024-02-29","arxiv_id":"2402.19326","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":5,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"6 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/generalizable-whole-slide-image#ran","syntology_url":"https://syntology.ai/paper/2402.19326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.19326"}},"official":{"repos":["ls1rius/wsi_five"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/classes-are-not-equal-an-empirical-study-on","slug":"classes-are-not-equal-an-empirical-study-on","title":"Classes Are Not Equal: An Empirical Study on Image Recognition Fairness","date":"2024-02-28","arxiv_id":"2402.18133","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/classes-are-not-equal-an-empirical-study-on#ran","syntology_url":"https://syntology.ai/paper/2402.18133","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18133"}},"official":{"repos":["dvlab-research/parametric-contrastive-learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/decisionnce-embodied-multimodal","slug":"decisionnce-embodied-multimodal","title":"DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning","date":"2024-02-28","arxiv_id":"2402.18137","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/decisionnce-embodied-multimodal#ran","syntology_url":"https://syntology.ai/paper/2402.18137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18137"}},"official":{"repos":["2toinf/DecisionNCE"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-based-neural-network-weights","slug":"diffusion-based-neural-network-weights","title":"Diffusion-Based Neural Network Weights Generation","date":"2024-02-28","arxiv_id":"2402.18153","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/diffusion-based-neural-network-weights#ran","syntology_url":"https://syntology.ai/paper/2402.18153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18153"}},"official":{"repos":["sorobedio/dnnwg"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/demonstrating-and-reducing-shortcuts-in","slug":"demonstrating-and-reducing-shortcuts-in","title":"Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning","date":"2024-02-27","arxiv_id":"2402.17510","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/demonstrating-and-reducing-shortcuts-in#ran","syntology_url":"https://syntology.ai/paper/2402.17510","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17510"}},"official":{"repos":["mauritsbleeker/svl-framework"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unlocking-the-power-of-large-language-models","slug":"unlocking-the-power-of-large-language-models","title":"Unlocking the Power of Large Language Models for Entity Alignment","date":"2024-02-23","arxiv_id":"2402.15048","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unlocking-the-power-of-large-language-models#ran","syntology_url":"https://syntology.ai/paper/2402.15048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15048"}},"official":{"repos":["jxh4945777/ChatEA"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unigraph-learning-a-cross-domain-graph","slug":"unigraph-learning-a-cross-domain-graph","title":"UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs","date":"2024-02-21","arxiv_id":"2402.13630","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":10,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unigraph-learning-a-cross-domain-graph#ran","syntology_url":"https://syntology.ai/paper/2402.13630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.13630"}},"official":{"repos":["yf-he/UniGraph"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/separating-common-from-salient-patterns-with","slug":"separating-common-from-salient-patterns-with","title":"Separating common from salient patterns with Contrastive Representation Learning","date":"2024-02-19","arxiv_id":"2402.11928","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/separating-common-from-salient-patterns-with#ran","syntology_url":"https://syntology.ai/paper/2402.11928","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11928"}},"official":{"repos":["neurospin-projects/2024_rlouiset_sep_clr"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/polyhedral-complex-derivation-from-piecewise","slug":"polyhedral-complex-derivation-from-piecewise","title":"Polyhedral Complex Derivation from Piecewise Trilinear Networks","date":"2024-02-16","arxiv_id":"2402.10403","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/polyhedral-complex-derivation-from-piecewise#ran","syntology_url":"https://syntology.ai/paper/2402.10403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10403"}},"official":{"repos":["naver-ai/tropical-nerf.pytorch"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/parametric-augmentation-for-time-series","slug":"parametric-augmentation-for-time-series","title":"Parametric Augmentation for Time Series Contrastive Learning","date":"2024-02-16","arxiv_id":"2402.10434","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":3,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":4,"n_no_contract":3,"n_pointer_only":8,"phrase":"7 ran (of which 3 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 4 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/parametric-augmentation-for-time-series#ran","syntology_url":"https://syntology.ai/paper/2402.10434","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10434"}},"official":{"repos":["AslanDing/AutoTCL"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":3,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pixel-sentence-representation-learning","slug":"pixel-sentence-representation-learning","title":"Pixel Sentence Representation Learning","date":"2024-02-13","arxiv_id":"2402.08183","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pixel-sentence-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2402.08183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.08183"}},"official":{"repos":["gowitheflow-1998/pixel-linguist"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/randumb-a-simple-approach-that-questions-the","slug":"randumb-a-simple-approach-that-questions-the","title":"Random Representations Outperform Online Continually Learned Representations","date":"2024-02-13","arxiv_id":"2402.08823","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/randumb-a-simple-approach-that-questions-the#ran","syntology_url":"https://syntology.ai/paper/2402.08823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.08823"}},"official":{"repos":["drimpossible/randumb"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-correcting-self-consuming-loops-for","slug":"self-correcting-self-consuming-loops-for","title":"Self-Correcting Self-Consuming Loops for Generative Model Training","date":"2024-02-11","arxiv_id":"2402.07087","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/self-correcting-self-consuming-loops-for#ran","syntology_url":"https://syntology.ai/paper/2402.07087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07087"}},"official":{"repos":["nate-gillman/self-correcting-self-consuming"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/premier-taco-pretraining-multitask","slug":"premier-taco-pretraining-multitask","title":"Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss","date":"2024-02-09","arxiv_id":"2402.06187","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/premier-taco-pretraining-multitask#ran","syntology_url":"https://syntology.ai/paper/2402.06187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.06187"}},"official":{"repos":["premiertaco/premier-taco"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-graph-information-bottleneck","slug":"dynamic-graph-information-bottleneck","title":"Dynamic Graph Information Bottleneck","date":"2024-02-09","arxiv_id":"2402.06716","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dynamic-graph-information-bottleneck#ran","syntology_url":"https://syntology.ai/paper/2402.06716","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.06716"}},"official":{"repos":["ringbdstack/dgib"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-patch-prediction-adapting-llms-for-time","slug":"multi-patch-prediction-adapting-llms-for-time","title":"Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning","date":"2024-02-07","arxiv_id":"2402.04852","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multi-patch-prediction-adapting-llms-for-time#ran","syntology_url":"https://syntology.ai/paper/2402.04852","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.04852"}},"official":{"repos":["yxbian23/aLLM4TS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/two-trades-is-not-baffled-condense-graph-via","slug":"two-trades-is-not-baffled-condense-graph-via","title":"Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching","date":"2024-02-07","arxiv_id":"2402.04924","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/two-trades-is-not-baffled-condense-graph-via#ran","syntology_url":"https://syntology.ai/paper/2402.04924","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.04924"}},"official":{"repos":["nus-hpc-ai-lab/ctrl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-with-mixture-of-prototypes-for-out","slug":"learning-with-mixture-of-prototypes-for-out","title":"Learning with Mixture of Prototypes for Out-of-Distribution Detection","date":"2024-02-05","arxiv_id":"2402.02653","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/learning-with-mixture-of-prototypes-for-out#ran","syntology_url":"https://syntology.ai/paper/2402.02653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02653"}},"official":{"repos":["jeff024/palm"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/minimum-description-length-and-generalization-1","slug":"minimum-description-length-and-generalization-1","title":"Minimum Description Length and Generalization Guarantees for Representation Learning","date":"2024-02-05","arxiv_id":"2402.03254","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/minimum-description-length-and-generalization-1#ran","syntology_url":"https://syntology.ai/paper/2402.03254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03254"}},"official":{"repos":["piotrkrasnowski/mdl_and_generalization_guarantees_for_representation_learning"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/stereographic-spherical-sliced-wasserstein","slug":"stereographic-spherical-sliced-wasserstein","title":"Stereographic Spherical Sliced Wasserstein Distances","date":"2024-02-04","arxiv_id":"2402.02345","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stereographic-spherical-sliced-wasserstein#ran","syntology_url":"https://syntology.ai/paper/2402.02345","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02345"}},"official":{"repos":["mint-vu/s3wd"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/beclr-batch-enhanced-contrastive-few-shot","slug":"beclr-batch-enhanced-contrastive-few-shot","title":"BECLR: Batch Enhanced Contrastive Few-Shot Learning","date":"2024-02-04","arxiv_id":"2402.02444","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/beclr-batch-enhanced-contrastive-few-shot#ran","syntology_url":"https://syntology.ai/paper/2402.02444","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02444"}},"official":{"repos":["stypoumic/beclr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-graph-is-worth-k-words-euclideanizing-graph","slug":"a-graph-is-worth-k-words-euclideanizing-graph","title":"A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer","date":"2024-02-04","arxiv_id":"2402.02464","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-graph-is-worth-k-words-euclideanizing-graph#ran","syntology_url":"https://syntology.ai/paper/2402.02464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02464"}},"official":{"repos":["A4Bio/GraphsGPT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-probabilistic-model-to-explain-self","slug":"a-probabilistic-model-to-explain-self","title":"A Probabilistic Model Behind Self-Supervised Learning","date":"2024-02-02","arxiv_id":"2402.01399","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-probabilistic-model-to-explain-self#ran","syntology_url":"https://syntology.ai/paper/2402.01399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01399"}},"official":{"repos":["alicebizeul/simvae"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-view-masked-diffusion-transformers-for","slug":"cross-view-masked-diffusion-transformers-for","title":"Cross-view Masked Diffusion Transformers for Person Image Synthesis","date":"2024-02-02","arxiv_id":"2402.01516","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/cross-view-masked-diffusion-transformers-for#ran","syntology_url":"https://syntology.ai/paper/2402.01516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01516"}},"official":{"repos":["trungpx/xmdpt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/l2g2g-a-scalable-local-to-global-network","slug":"l2g2g-a-scalable-local-to-global-network","title":"L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders","date":"2024-02-02","arxiv_id":"2402.01614","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/l2g2g-a-scalable-local-to-global-network#ran","syntology_url":"https://syntology.ai/paper/2402.01614","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01614"}},"official":{"repos":["tonyauyeung/local2gae2global"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/guiding-masked-representation-learning-to","slug":"guiding-masked-representation-learning-to","title":"Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram","date":"2024-02-02","arxiv_id":"2402.09450","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":5,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":12,"phrase":"9 ran (of which 5 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/guiding-masked-representation-learning-to#ran","syntology_url":"https://syntology.ai/paper/2402.09450","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09450"}},"official":{"repos":["bakqui/st-mem"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":5,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/score-based-causal-representation-learning-1","slug":"score-based-causal-representation-learning-1","title":"Score-based Causal Representation Learning: Linear and General Transformations","date":"2024-02-01","arxiv_id":"2402.00849","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/score-based-causal-representation-learning-1#ran","syntology_url":"https://syntology.ai/paper/2402.00849","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.00849"}},"official":{"repos":["acarturk-e/score-based-crl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-contrastive-learning-with-cohesive","slug":"graph-contrastive-learning-with-cohesive","title":"Graph Contrastive Learning with Cohesive Subgraph Awareness","date":"2024-01-31","arxiv_id":"2401.17580","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/graph-contrastive-learning-with-cohesive#ran","syntology_url":"https://syntology.ai/paper/2401.17580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17580"}},"official":{"repos":["wuyucheng2002/ctaug"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-machine-learning-for-cost-effective","slug":"causal-machine-learning-for-cost-effective","title":"Causal Machine Learning for Cost-Effective Allocation of Development Aid","date":"2024-01-30","arxiv_id":"2401.16986","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/causal-machine-learning-for-cost-effective#ran","syntology_url":"https://syntology.ai/paper/2401.16986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.16986"}},"official":{"repos":["mkuzma96/cg-ct"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/zero-shot-reinforcement-learning-via-function","slug":"zero-shot-reinforcement-learning-via-function","title":"Zero-Shot Reinforcement Learning via Function Encoders","date":"2024-01-30","arxiv_id":"2401.17173","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/zero-shot-reinforcement-learning-via-function#ran","syntology_url":"https://syntology.ai/paper/2401.17173","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17173"}},"official":{"repos":["anonymousresearcher5642/functionencoderrl","tyler-ingebrand/functionencoderrl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-scale-mae-a-tale-of-multi-scale","slug":"cross-scale-mae-a-tale-of-multi-scale","title":"Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing","date":"2024-01-29","arxiv_id":"2401.15855","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cross-scale-mae-a-tale-of-multi-scale#ran","syntology_url":"https://syntology.ai/paper/2401.15855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.15855"}},"official":{"repos":["aicip/Cross-Scale-MAE"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-embedding-clustering-driven-by-sample","slug":"deep-embedding-clustering-driven-by-sample","title":"Deep Embedding Clustering Driven by Sample Stability","date":"2024-01-29","arxiv_id":"2401.15989","repositories_listed":0,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":7,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/deep-embedding-clustering-driven-by-sample#ran","syntology_url":"https://syntology.ai/paper/2401.15989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.15989"}},"official":null}},{"url":"/paper/recdcl-dual-contrastive-learning-for","slug":"recdcl-dual-contrastive-learning-for","title":"RecDCL: Dual Contrastive Learning for Recommendation","date":"2024-01-28","arxiv_id":"2401.15635","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/recdcl-dual-contrastive-learning-for#ran","syntology_url":"https://syntology.ai/paper/2401.15635","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.15635"}},"official":{"repos":["thudm/recdcl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/product-manifold-representations-for-learning","slug":"product-manifold-representations-for-learning","title":"Product Manifold Representations for Learning on Biological Pathways","date":"2024-01-27","arxiv_id":"2401.15478","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/product-manifold-representations-for-learning#ran","syntology_url":"https://syntology.ai/paper/2401.15478","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.15478"}},"official":{"repos":["mcneela/mixed-curvature-gcn","mcneela/mixed-curvature-pathways"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deconstructing-denoising-diffusion-models-for","slug":"deconstructing-denoising-diffusion-models-for","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","date":"2024-01-25","arxiv_id":"2401.14404","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":2,"n_honours":4,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 4 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deconstructing-denoising-diffusion-models-for#ran","syntology_url":"https://syntology.ai/paper/2401.14404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.14404"}},"official":null}}],"record_sha256":"bbbb627fd8d4faff988bc6c32a80303ad3a0862c08c34179793331abcf2bd190","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}