{"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/image-classification/papers/ran/5","list_of":"/task/image-classification","task":"Image Classification","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":5,"pages_in_order":14,"rows_per_page":100,"rows":[401,500],"of":1392,"counts":{"archive_papers_tagged":10488,"with_a_code_link":4702,"where_syntology_ran_a_sample":1392,"not_listed_spam_title":0,"listed":10488,"listed_where_code_ran":1392,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1164,"every_run_a_failure_of_syntologys_instrument":228,"listed_with_a_run_with_no_instrument_failure":1164,"listed_every_run_a_failure_of_syntologys_instrument":228,"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/image-classification/papers/ran/1","prev":"/task/image-classification/papers/ran/4","next":"/task/image-classification/papers/ran/6","papers":[{"url":"/paper/medvit-a-robust-vision-transformer-for","slug":"medvit-a-robust-vision-transformer-for","title":"MedViT: A Robust Vision Transformer for Generalized Medical Image Classification","date":"2023-02-19","arxiv_id":"2302.09462","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":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) · 1 unverified","sample_list":"/paper/medvit-a-robust-vision-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2302.09462","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.09462"}},"official":{"repos":["Omid-Nejati/MedViT"],"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/thc-accelerating-distributed-deep-learning","slug":"thc-accelerating-distributed-deep-learning","title":"THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression","date":"2023-02-16","arxiv_id":"2302.08545","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":1,"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/thc-accelerating-distributed-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2302.08545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.08545"}},"official":{"repos":["sophiali06/byteps_thc"],"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/meta-album-multi-domain-meta-dataset-for-few-1","slug":"meta-album-multi-domain-meta-dataset-for-few-1","title":"Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification","date":"2023-02-16","arxiv_id":"2302.08909","repositories_listed":3,"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/meta-album-multi-domain-meta-dataset-for-few-1#ran","syntology_url":"https://syntology.ai/paper/2302.08909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.08909"}},"official":{"repos":["dustincarrion/cd-metadl","ihsaan-ullah/meta-album","ihsanullah2131/meta-album"],"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/improved-online-conformal-prediction-via","slug":"improved-online-conformal-prediction-via","title":"Improved Online Conformal Prediction via Strongly Adaptive Online Learning","date":"2023-02-15","arxiv_id":"2302.07869","repositories_listed":2,"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":1,"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/improved-online-conformal-prediction-via#ran","syntology_url":"https://syntology.ai/paper/2302.07869","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.07869"}},"official":{"repos":["salesforce/online_conformal"],"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/stitchable-neural-networks","slug":"stitchable-neural-networks","title":"Stitchable Neural Networks","date":"2023-02-13","arxiv_id":"2302.06586","repositories_listed":2,"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":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) · 1 unverified","sample_list":"/paper/stitchable-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2302.06586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.06586"}},"official":{"repos":["ziplab/SN-Net"],"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/symbolic-discovery-of-optimization-algorithms-1","slug":"symbolic-discovery-of-optimization-algorithms-1","title":"Symbolic Discovery of Optimization Algorithms","date":"2023-02-13","arxiv_id":"2302.06675","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/symbolic-discovery-of-optimization-algorithms-1#ran","syntology_url":"https://syntology.ai/paper/2302.06675","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.06675"}},"official":null}},{"url":"/paper/lit-tuned-models-for-efficient-species","slug":"lit-tuned-models-for-efficient-species","title":"LiT Tuned Models for Efficient Species Detection","date":"2023-02-12","arxiv_id":"2302.10281","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/lit-tuned-models-for-efficient-species#ran","syntology_url":"https://syntology.ai/paper/2302.10281","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.10281"}},"official":{"repos":["NYU-DICE-Lab/open_clip"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/reversible-vision-transformers-1","slug":"reversible-vision-transformers-1","title":"Reversible Vision Transformers","date":"2023-02-09","arxiv_id":"2302.04869","repositories_listed":4,"syntology":{"n":29,"n_ran":24,"n_constructed":5,"n_ran_checked":23,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":23,"n_pointer_only":26,"phrase":"24 ran (of which 5 constructed an object rather than computing a result; 23 with no instrument failure: 0 honoured, 0 violated, 23 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/reversible-vision-transformers-1#ran","syntology_url":"https://syntology.ai/paper/2302.04869","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.04869"}},"official":{"repos":["karttikeya/minrev","facebookresearch/SlowFast","facebookresearch/mvit"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":5,"n_ran_no_instrument_failure":21,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/cross-layer-retrospective-retrieving-via","slug":"cross-layer-retrospective-retrieving-via","title":"Cross-Layer Retrospective Retrieving via Layer Attention","date":"2023-02-08","arxiv_id":"2302.03985","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/cross-layer-retrospective-retrieving-via#ran","syntology_url":"https://syntology.ai/paper/2302.03985","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.03985"}},"official":{"repos":["joyfang1106/mrla"],"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/effective-data-augmentation-with-diffusion","slug":"effective-data-augmentation-with-diffusion","title":"Effective Data Augmentation With Diffusion Models","date":"2023-02-07","arxiv_id":"2302.07944","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":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) · 1 unverified","sample_list":"/paper/effective-data-augmentation-with-diffusion#ran","syntology_url":"https://syntology.ai/paper/2302.07944","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.07944"}},"official":{"repos":["brandontrabucco/da-fusion"],"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/chils-zero-shot-image-classification-with","slug":"chils-zero-shot-image-classification-with","title":"CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets","date":"2023-02-06","arxiv_id":"2302.02551","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"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, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/chils-zero-shot-image-classification-with#ran","syntology_url":"https://syntology.ai/paper/2302.02551","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.02551"}},"official":{"repos":["acmi-lab/chils"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-discriminative-vs-generative","slug":"revisiting-discriminative-vs-generative","title":"Revisiting Discriminative vs. Generative Classifiers: Theory and Implications","date":"2023-02-05","arxiv_id":"2302.02334","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":3,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/revisiting-discriminative-vs-generative#ran","syntology_url":"https://syntology.ai/paper/2302.02334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.02334"}},"official":{"repos":["ML-GSAI/Revisiting-Dis-vs-Gen-Classifiers"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/cospgd-a-unified-white-box-adversarial-attack","slug":"cospgd-a-unified-white-box-adversarial-attack","title":"CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks","date":"2023-02-04","arxiv_id":"2302.02213","repositories_listed":2,"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":2,"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/cospgd-a-unified-white-box-adversarial-attack#ran","syntology_url":"https://syntology.ai/paper/2302.02213","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.02213"}},"official":{"repos":["shashankskagnihotri/adv-corrected-ddcat-cospgd","shashankskagnihotri/cospgd"],"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/on-the-efficacy-of-differentially-private-few","slug":"on-the-efficacy-of-differentially-private-few","title":"On the Efficacy of Differentially Private Few-shot Image Classification","date":"2023-02-02","arxiv_id":"2302.01190","repositories_listed":1,"syntology":{"n":16,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":12,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 1 violated, 12 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-the-efficacy-of-differentially-private-few#ran","syntology_url":"https://syntology.ai/paper/2302.01190","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.01190"}},"official":{"repos":["cambridge-mlg/dp-few-shot"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/continual-learning-with-scaled-gradient","slug":"continual-learning-with-scaled-gradient","title":"Continual Learning with Scaled Gradient Projection","date":"2023-02-02","arxiv_id":"2302.01386","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/continual-learning-with-scaled-gradient#ran","syntology_url":"https://syntology.ai/paper/2302.01386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.01386"}},"official":{"repos":["sahagobinda/sgp"],"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/mplug-2-a-modularized-multi-modal-foundation","slug":"mplug-2-a-modularized-multi-modal-foundation","title":"mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video","date":"2023-02-01","arxiv_id":"2302.00402","repositories_listed":4,"syntology":{"n":19,"n_ran":17,"n_constructed":0,"n_ran_checked":9,"n_instrument":8,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 8 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mplug-2-a-modularized-multi-modal-foundation#ran","syntology_url":"https://syntology.ai/paper/2302.00402","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.00402"}},"official":{"repos":["alibaba/AliceMind"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/np-match-towards-a-new-probabilistic-model","slug":"np-match-towards-a-new-probabilistic-model","title":"NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning","date":"2023-01-31","arxiv_id":"2301.13569","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/np-match-towards-a-new-probabilistic-model#ran","syntology_url":"https://syntology.ai/paper/2301.13569","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.13569"}},"official":{"repos":["jianf-wang/np-match"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/seaformer-squeeze-enhanced-axial-transformer","slug":"seaformer-squeeze-enhanced-axial-transformer","title":"SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual Recognition","date":"2023-01-30","arxiv_id":"2301.13156","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":2,"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/seaformer-squeeze-enhanced-axial-transformer#ran","syntology_url":"https://syntology.ai/paper/2301.13156","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.13156"}},"official":{"repos":["fudan-zvg/seaformer"],"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/direct-parameterization-of-lipschitz-bounded","slug":"direct-parameterization-of-lipschitz-bounded","title":"Direct Parameterization of Lipschitz-Bounded Deep Networks","date":"2023-01-27","arxiv_id":"2301.11526","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/direct-parameterization-of-lipschitz-bounded#ran","syntology_url":"https://syntology.ai/paper/2301.11526","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11526"}},"official":{"repos":["acfr/lbdn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/learning-to-unlearn-instance-wise-unlearning","slug":"learning-to-unlearn-instance-wise-unlearning","title":"Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers","date":"2023-01-27","arxiv_id":"2301.11578","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-to-unlearn-instance-wise-unlearning#ran","syntology_url":"https://syntology.ai/paper/2301.11578","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11578"}},"official":{"repos":["csm9493/L2UL"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/zico-zero-shot-nas-via-inverse-coefficient-of","slug":"zico-zero-shot-nas-via-inverse-coefficient-of","title":"ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients","date":"2023-01-26","arxiv_id":"2301.11300","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/zico-zero-shot-nas-via-inverse-coefficient-of#ran","syntology_url":"https://syntology.ai/paper/2301.11300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11300"}},"official":{"repos":["SLDGroup/ZiCo"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/navigating-the-pitfalls-of-active-learning-1","slug":"navigating-the-pitfalls-of-active-learning-1","title":"Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment","date":"2023-01-25","arxiv_id":"2301.10625","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":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) · 1 unverified","sample_list":"/paper/navigating-the-pitfalls-of-active-learning-1#ran","syntology_url":"https://syntology.ai/paper/2301.10625","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.10625"}},"official":{"repos":["iml-dkfz/realistic-al"],"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/modeling-uncertain-feature-representation-for","slug":"modeling-uncertain-feature-representation-for","title":"Modeling Uncertain Feature Representation for Domain Generalization","date":"2023-01-16","arxiv_id":"2301.06442","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 3 unverified","sample_list":"/paper/modeling-uncertain-feature-representation-for#ran","syntology_url":"https://syntology.ai/paper/2301.06442","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.06442"}},"official":{"repos":["lixiaotong97/dsu"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/a-stochastic-proximal-polyak-step-size","slug":"a-stochastic-proximal-polyak-step-size","title":"A Stochastic Proximal Polyak Step Size","date":"2023-01-12","arxiv_id":"2301.04935","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/a-stochastic-proximal-polyak-step-size#ran","syntology_url":"https://syntology.ai/paper/2301.04935","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.04935"}},"official":{"repos":["fabian-sp/ProxSPS"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-grained-encoder-for-vision-1","slug":"dynamic-grained-encoder-for-vision-1","title":"Dynamic Grained Encoder for Vision Transformers","date":"2023-01-10","arxiv_id":"2301.03831","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"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) · 4 unverified","sample_list":"/paper/dynamic-grained-encoder-for-vision-1#ran","syntology_url":"https://syntology.ai/paper/2301.03831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.03831"}},"official":{"repos":["stevengrove/vtpack"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/designing-bert-for-convolutional-networks","slug":"designing-bert-for-convolutional-networks","title":"Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling","date":"2023-01-09","arxiv_id":"2301.03580","repositories_listed":2,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/designing-bert-for-convolutional-networks#ran","syntology_url":"https://syntology.ai/paper/2301.03580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.03580"}},"official":{"repos":["keyu-tian/spark"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/tinymim-an-empirical-study-of-distilling-mim","slug":"tinymim-an-empirical-study-of-distilling-mim","title":"TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models","date":"2023-01-03","arxiv_id":"2301.01296","repositories_listed":2,"syntology":{"n":11,"n_ran":8,"n_constructed":5,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"8 ran (of which 5 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tinymim-an-empirical-study-of-distilling-mim#ran","syntology_url":"https://syntology.ai/paper/2301.01296","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.01296"}},"official":{"repos":["oliverrensu/tinymim"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/learning-multimodal-data-augmentation-in","slug":"learning-multimodal-data-augmentation-in","title":"Learning Multimodal Data Augmentation in Feature Space","date":"2022-12-29","arxiv_id":"2212.14453","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":1,"n_ran_checked":3,"n_instrument":6,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":1,"n_pointer_only":0,"phrase":"9 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 2 violated, 1 with no contract checked; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-multimodal-data-augmentation-in#ran","syntology_url":"https://syntology.ai/paper/2212.14453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.14453"}},"official":{"repos":["lzcemma/lemda"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/reversible-column-networks","slug":"reversible-column-networks","title":"Reversible Column Networks","date":"2022-12-22","arxiv_id":"2212.11696","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":8,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 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; 0 where Syntology's instrument failed) · 3 unverified; every one of the 8 samples that ran constructed an object rather than computing a result","sample_list":"/paper/reversible-column-networks#ran","syntology_url":"https://syntology.ai/paper/2212.11696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.11696"}},"official":{"repos":["megvii-research/revcol"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":8,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/better-may-not-be-fairer-can-data","slug":"better-may-not-be-fairer-can-data","title":"Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classification","date":"2022-12-16","arxiv_id":"2212.08649","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/better-may-not-be-fairer-can-data#ran","syntology_url":"https://syntology.ai/paper/2212.08649","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.08649"}},"official":{"repos":["charismaticchiu/Better-May-Not-Be-Fairer-A-Study-Study-on-Subgroup-Discrepancy-in-Image-Classification"],"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/bayesian-posterior-approximation-with","slug":"bayesian-posterior-approximation-with","title":"Bayesian posterior approximation with stochastic ensembles","date":"2022-12-15","arxiv_id":"2212.08123","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":1,"n_no_contract":3,"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, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bayesian-posterior-approximation-with#ran","syntology_url":"https://syntology.ai/paper/2212.08123","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.08123"}},"official":{"repos":["oleksandr-balabanov/stochastic-ensembles"],"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/domain-generalization-by-learning-and","slug":"domain-generalization-by-learning-and","title":"Domain Generalization by Learning and Removing Domain-specific Features","date":"2022-12-14","arxiv_id":"2212.07101","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/domain-generalization-by-learning-and#ran","syntology_url":"https://syntology.ai/paper/2212.07101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.07101"}},"official":{"repos":["yulearningg/LRDG"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/reproducible-scaling-laws-for-contrastive","slug":"reproducible-scaling-laws-for-contrastive","title":"Reproducible scaling laws for contrastive language-image learning","date":"2022-12-14","arxiv_id":"2212.07143","repositories_listed":5,"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":3,"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/reproducible-scaling-laws-for-contrastive#ran","syntology_url":"https://syntology.ai/paper/2212.07143","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.07143"}},"official":{"repos":["laion-ai/scaling-laws-openclip"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/post-hoc-uncertainty-learning-using-a","slug":"post-hoc-uncertainty-learning-using-a","title":"Post-hoc Uncertainty Learning using a Dirichlet Meta-Model","date":"2022-12-14","arxiv_id":"2212.07359","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"8 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/post-hoc-uncertainty-learning-using-a#ran","syntology_url":"https://syntology.ai/paper/2212.07359","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.07359"}},"official":{"repos":["maohaos2/PosthocUQ"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-self-supervised-learning-with","slug":"efficient-self-supervised-learning-with","title":"Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language","date":"2022-12-14","arxiv_id":"2212.07525","repositories_listed":5,"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":2,"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/efficient-self-supervised-learning-with#ran","syntology_url":"https://syntology.ai/paper/2212.07525","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.07525"}},"official":{"repos":["facebookresearch/fairseq"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/losses-over-labels-weakly-supervised-learning","slug":"losses-over-labels-weakly-supervised-learning","title":"Losses over Labels: Weakly Supervised Learning via Direct Loss Construction","date":"2022-12-13","arxiv_id":"2212.06921","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"5 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/losses-over-labels-weakly-supervised-learning#ran","syntology_url":"https://syntology.ai/paper/2212.06921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.06921"}},"official":{"repos":["dsam99/lol"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/poda-prompt-driven-zero-shot-domain","slug":"poda-prompt-driven-zero-shot-domain","title":"PØDA: Prompt-driven Zero-shot Domain Adaptation","date":"2022-12-06","arxiv_id":"2212.03241","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/poda-prompt-driven-zero-shot-domain#ran","syntology_url":"https://syntology.ai/paper/2212.03241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.03241"}},"official":{"repos":["astra-vision/poda"],"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/location-aware-self-supervised-transformers","slug":"location-aware-self-supervised-transformers","title":"Location-Aware Self-Supervised Transformers for Semantic Segmentation","date":"2022-12-05","arxiv_id":"2212.02400","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/location-aware-self-supervised-transformers#ran","syntology_url":"https://syntology.ai/paper/2212.02400","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.02400"}},"official":{"repos":["google-research/scenic"],"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/resformer-scaling-vits-with-multi-resolution","slug":"resformer-scaling-vits-with-multi-resolution","title":"ResFormer: Scaling ViTs with Multi-Resolution Training","date":"2022-12-01","arxiv_id":"2212.00776","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/resformer-scaling-vits-with-multi-resolution#ran","syntology_url":"https://syntology.ai/paper/2212.00776","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.00776"}},"official":{"repos":["ruitian12/resformer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/gennape-towards-generalized-neural","slug":"gennape-towards-generalized-neural","title":"GENNAPE: Towards Generalized Neural Architecture Performance Estimators","date":"2022-11-30","arxiv_id":"2211.17226","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":8,"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) · 2 unverified","sample_list":"/paper/gennape-towards-generalized-neural#ran","syntology_url":"https://syntology.ai/paper/2211.17226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.17226"}},"official":{"repos":["Ascend-Research/GENNAPE"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/curriculum-temperature-for-knowledge","slug":"curriculum-temperature-for-knowledge","title":"Curriculum Temperature for Knowledge Distillation","date":"2022-11-29","arxiv_id":"2211.16231","repositories_listed":1,"syntology":{"n":15,"n_ran":8,"n_constructed":0,"n_ran_checked":3,"n_instrument":5,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"8 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; 5 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/curriculum-temperature-for-knowledge#ran","syntology_url":"https://syntology.ai/paper/2211.16231","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.16231"}},"official":{"repos":["zhengli97/ctkd"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/eurnet-efficient-multi-range-relational","slug":"eurnet-efficient-multi-range-relational","title":"EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data","date":"2022-11-23","arxiv_id":"2211.12941","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":4,"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) · 4 unverified","sample_list":"/paper/eurnet-efficient-multi-range-relational#ran","syntology_url":"https://syntology.ai/paper/2211.12941","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.12941"}},"official":{"repos":["hirl-team/eurnet-image"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/hyperbolic-sliced-wasserstein-via-geodesic","slug":"hyperbolic-sliced-wasserstein-via-geodesic","title":"Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections","date":"2022-11-18","arxiv_id":"2211.10066","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":5,"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/hyperbolic-sliced-wasserstein-via-geodesic#ran","syntology_url":"https://syntology.ai/paper/2211.10066","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.10066"}},"official":{"repos":["clbonet/Hyperbolic_Sliced-Wasserstein_via_Geodesic_and_Horospherical_Projections"],"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/cross-domain-federated-adaptive-prompt-tuning","slug":"cross-domain-federated-adaptive-prompt-tuning","title":"Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning","date":"2022-11-15","arxiv_id":"2211.07864","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/cross-domain-federated-adaptive-prompt-tuning#ran","syntology_url":"https://syntology.ai/paper/2211.07864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.07864"}},"official":{"repos":["leondada/fedapt"],"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/eva-exploring-the-limits-of-masked-visual","slug":"eva-exploring-the-limits-of-masked-visual","title":"EVA: Exploring the Limits of Masked Visual Representation Learning at Scale","date":"2022-11-14","arxiv_id":"2211.07636","repositories_listed":6,"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":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) · 2 unverified","sample_list":"/paper/eva-exploring-the-limits-of-masked-visual#ran","syntology_url":"https://syntology.ai/paper/2211.07636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.07636"}},"official":{"repos":["baaivision/eva","rwightman/pytorch-image-models"],"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/altclip-altering-the-language-encoder-in-clip","slug":"altclip-altering-the-language-encoder-in-clip","title":"AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities","date":"2022-11-12","arxiv_id":"2211.06679","repositories_listed":2,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/altclip-altering-the-language-encoder-in-clip#ran","syntology_url":"https://syntology.ai/paper/2211.06679","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.06679"}},"official":{"repos":["flagai-open/flagai"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/internimage-exploring-large-scale-vision","slug":"internimage-exploring-large-scale-vision","title":"InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions","date":"2022-11-10","arxiv_id":"2211.05778","repositories_listed":3,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/internimage-exploring-large-scale-vision#ran","syntology_url":"https://syntology.ai/paper/2211.05778","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.05778"}},"official":{"repos":["opengvlab/internimage"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"url":"/paper/detecting-shortcuts-in-medical-images-a-case","slug":"detecting-shortcuts-in-medical-images-a-case","title":"Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays","date":"2022-11-08","arxiv_id":"2211.04279","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"7 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/detecting-shortcuts-in-medical-images-a-case#ran","syntology_url":"https://syntology.ai/paper/2211.04279","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.04279"}},"official":{"repos":["ameliajimenez/shortcuts-chest-xray"],"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":["listed","official"]}}},{"url":"/paper/efficient-multi-order-gated-aggregation","slug":"efficient-multi-order-gated-aggregation","title":"MogaNet: Multi-order Gated Aggregation Network","date":"2022-11-07","arxiv_id":"2211.03295","repositories_listed":7,"syntology":{"n":15,"n_ran":12,"n_constructed":7,"n_ran_checked":10,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"12 ran (of which 7 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/efficient-multi-order-gated-aggregation#ran","syntology_url":"https://syntology.ai/paper/2211.03295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.03295"}},"official":{"repos":["Westlake-AI/MogaNet","Westlake-AI/openmixup","chengtan9907/OpenSTL"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":7,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/chinese-clip-contrastive-vision-language","slug":"chinese-clip-contrastive-vision-language","title":"Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese","date":"2022-11-02","arxiv_id":"2211.01335","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"7 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chinese-clip-contrastive-vision-language#ran","syntology_url":"https://syntology.ai/paper/2211.01335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.01335"}},"official":{"repos":["ofa-sys/chinese-clip"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/geo-sic-learning-deformable-geometric-shapes","slug":"geo-sic-learning-deformable-geometric-shapes","title":"Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers","date":"2022-10-25","arxiv_id":"2210.13704","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/geo-sic-learning-deformable-geometric-shapes#ran","syntology_url":"https://syntology.ai/paper/2210.13704","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13704"}},"official":{"repos":["jw4hv/geo-sic"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/swift-rapid-decentralized-federated-learning","slug":"swift-rapid-decentralized-federated-learning","title":"SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication","date":"2022-10-25","arxiv_id":"2210.14026","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/swift-rapid-decentralized-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2210.14026","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.14026"}},"official":{"repos":["umd-huang-lab/SWIFT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-sparse-convolutional-model-for","slug":"revisiting-sparse-convolutional-model-for","title":"Revisiting Sparse Convolutional Model for Visual Recognition","date":"2022-10-24","arxiv_id":"2210.12945","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/revisiting-sparse-convolutional-model-for#ran","syntology_url":"https://syntology.ai/paper/2210.12945","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12945"}},"official":{"repos":["delay-xili/sdnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/metaformer-baselines-for-vision","slug":"metaformer-baselines-for-vision","title":"MetaFormer Baselines for Vision","date":"2022-10-24","arxiv_id":"2210.13452","repositories_listed":8,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/metaformer-baselines-for-vision#ran","syntology_url":"https://syntology.ai/paper/2210.13452","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13452"}},"official":{"repos":["rwightman/pytorch-image-models","sail-sg/metaformer"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/provably-learning-diverse-features-in-multi","slug":"provably-learning-diverse-features-in-multi","title":"Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup","date":"2022-10-24","arxiv_id":"2210.13512","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":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) · 0 unverified","sample_list":"/paper/provably-learning-diverse-features-in-multi#ran","syntology_url":"https://syntology.ai/paper/2210.13512","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13512"}},"official":{"repos":["2014mchidamb/midpoint-mixup-multi-view-icml"],"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/croco-self-supervised-pre-training-for-3d","slug":"croco-self-supervised-pre-training-for-3d","title":"CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion","date":"2022-10-19","arxiv_id":"2210.10716","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":7,"n_ran_checked":8,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"12 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/croco-self-supervised-pre-training-for-3d#ran","syntology_url":"https://syntology.ai/paper/2210.10716","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.10716"}},"official":{"repos":["naver/croco"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":7,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/pareto-manifold-learning-tackling-multiple","slug":"pareto-manifold-learning-tackling-multiple","title":"Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models","date":"2022-10-18","arxiv_id":"2210.09759","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/pareto-manifold-learning-tackling-multiple#ran","syntology_url":"https://syntology.ai/paper/2210.09759","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09759"}},"official":{"repos":["nik-dim/pamal"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/scaling-shifting-your-features-a-new-baseline","slug":"scaling-shifting-your-features-a-new-baseline","title":"Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning","date":"2022-10-17","arxiv_id":"2210.08823","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":6,"n_instrument":6,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":11,"phrase":"12 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; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/scaling-shifting-your-features-a-new-baseline#ran","syntology_url":"https://syntology.ai/paper/2210.08823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.08823"}},"official":{"repos":["dongzelian/ssf"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/packed-ensembles-for-efficient-uncertainty","slug":"packed-ensembles-for-efficient-uncertainty","title":"Packed-Ensembles for Efficient Uncertainty Estimation","date":"2022-10-17","arxiv_id":"2210.09184","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":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) · 2 unverified","sample_list":"/paper/packed-ensembles-for-efficient-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2210.09184","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09184"}},"official":{"repos":["ENSTA-U2IS-AI/torch-uncertainty"],"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/multiple-instance-learning-via-iterative-self","slug":"multiple-instance-learning-via-iterative-self","title":"Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning","date":"2022-10-17","arxiv_id":"2210.09452","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/multiple-instance-learning-via-iterative-self#ran","syntology_url":"https://syntology.ai/paper/2210.09452","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09452"}},"official":{"repos":["kangningthu/its2clr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/tokenmixup-efficient-attention-guided-token","slug":"tokenmixup-efficient-attention-guided-token","title":"TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers","date":"2022-10-14","arxiv_id":"2210.07562","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/tokenmixup-efficient-attention-guided-token#ran","syntology_url":"https://syntology.ai/paper/2210.07562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07562"}},"official":{"repos":["mlvlab/tokenmixup"],"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/learnable-polyphase-sampling-for-shift","slug":"learnable-polyphase-sampling-for-shift","title":"Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks","date":"2022-10-14","arxiv_id":"2210.08001","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/learnable-polyphase-sampling-for-shift#ran","syntology_url":"https://syntology.ai/paper/2210.08001","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.08001"}},"official":{"repos":["raymondyeh07/learnable_polyphase_sampling"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ernie-layout-layout-knowledge-enhanced-pre","slug":"ernie-layout-layout-knowledge-enhanced-pre","title":"ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding","date":"2022-10-12","arxiv_id":"2210.06155","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":1,"phrase":"7 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ernie-layout-layout-knowledge-enhanced-pre#ran","syntology_url":"https://syntology.ai/paper/2210.06155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06155"}},"official":{"repos":["PaddlePaddle/PaddleNLP"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/latency-aware-spatial-wise-dynamic-networks","slug":"latency-aware-spatial-wise-dynamic-networks","title":"Latency-aware Spatial-wise Dynamic Networks","date":"2022-10-12","arxiv_id":"2210.06223","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":2,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/latency-aware-spatial-wise-dynamic-networks#ran","syntology_url":"https://syntology.ai/paper/2210.06223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06223"}},"official":{"repos":["leaplabthu/lasnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/understanding-the-failure-of-batch","slug":"understanding-the-failure-of-batch","title":"Understanding the Failure of Batch Normalization for Transformers in NLP","date":"2022-10-11","arxiv_id":"2210.05153","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":8,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"phrase":"9 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/understanding-the-failure-of-batch#ran","syntology_url":"https://syntology.ai/paper/2210.05153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05153"}},"official":{"repos":["wjxts/regularizedbn"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":8,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/opera-omni-supervised-representation-learning","slug":"opera-omni-supervised-representation-learning","title":"OPERA: Omni-Supervised Representation Learning with Hierarchical Supervisions","date":"2022-10-11","arxiv_id":"2210.05557","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/opera-omni-supervised-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2210.05557","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05557"}},"official":{"repos":["wangck20/opera"],"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","unlocated"]}}},{"url":"/paper/online-training-through-time-for-spiking","slug":"online-training-through-time-for-spiking","title":"Online Training Through Time for Spiking Neural Networks","date":"2022-10-09","arxiv_id":"2210.04195","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/online-training-through-time-for-spiking#ran","syntology_url":"https://syntology.ai/paper/2210.04195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.04195"}},"official":{"repos":["pkuxmq/ottt-snn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bi-directional-weakly-supervised-knowledge","slug":"bi-directional-weakly-supervised-knowledge","title":"Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification","date":"2022-10-07","arxiv_id":"2210.03664","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bi-directional-weakly-supervised-knowledge#ran","syntology_url":"https://syntology.ai/paper/2210.03664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.03664"}},"official":{"repos":["miccaiif/weno"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/clad-a-contrastive-learning-based-approach","slug":"clad-a-contrastive-learning-based-approach","title":"CLAD: A Contrastive Learning based Approach for Background Debiasing","date":"2022-10-06","arxiv_id":"2210.02748","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":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) · 1 unverified","sample_list":"/paper/clad-a-contrastive-learning-based-approach#ran","syntology_url":"https://syntology.ai/paper/2210.02748","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02748"}},"official":{"repos":["wangke97/clad"],"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/towards-a-unified-view-on-visual-parameter","slug":"towards-a-unified-view-on-visual-parameter","title":"Towards a Unified View on Visual Parameter-Efficient Transfer Learning","date":"2022-10-03","arxiv_id":"2210.00788","repositories_listed":1,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":8,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-a-unified-view-on-visual-parameter#ran","syntology_url":"https://syntology.ai/paper/2210.00788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.00788"}},"official":{"repos":["bruceyo/V-PETL"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lpt-long-tailed-prompt-tuning-for-image","slug":"lpt-long-tailed-prompt-tuning-for-image","title":"LPT: Long-tailed Prompt Tuning for Image Classification","date":"2022-10-03","arxiv_id":"2210.01033","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"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 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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/lpt-long-tailed-prompt-tuning-for-image#ran","syntology_url":"https://syntology.ai/paper/2210.01033","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.01033"}},"official":{"repos":["dongsky/lpt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/expediting-large-scale-vision-transformer-for","slug":"expediting-large-scale-vision-transformer-for","title":"Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning","date":"2022-10-03","arxiv_id":"2210.01035","repositories_listed":4,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"7 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/expediting-large-scale-vision-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2210.01035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.01035"}},"official":{"repos":["Expedit-LargeScale-Vision-Transformer/Expedit-DINO","Expedit-LargeScale-Vision-Transformer/Expedit-DPT","Expedit-LargeScale-Vision-Transformer/Expedit-Segmenter"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/concurrent-recognition-and-segmentation-with","slug":"concurrent-recognition-and-segmentation-with","title":"Learning Hierarchical Image Segmentation For Recognition and By Recognition","date":"2022-10-01","arxiv_id":"2210.00314","repositories_listed":1,"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/concurrent-recognition-and-segmentation-with#ran","syntology_url":"https://syntology.ai/paper/2210.00314","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.00314"}},"official":{"repos":["twke18/cast"],"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/mega-moving-average-equipped-gated-attention","slug":"mega-moving-average-equipped-gated-attention","title":"Mega: Moving Average Equipped Gated Attention","date":"2022-09-21","arxiv_id":"2209.10655","repositories_listed":7,"syntology":{"n":13,"n_ran":12,"n_constructed":6,"n_ran_checked":8,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"12 ran (of which 6 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mega-moving-average-equipped-gated-attention#ran","syntology_url":"https://syntology.ai/paper/2209.10655","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.10655"}},"official":{"repos":["facebookresearch/mega"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/enhance-the-visual-representation-via","slug":"enhance-the-visual-representation-via","title":"Enhance the Visual Representation via Discrete Adversarial Training","date":"2022-09-16","arxiv_id":"2209.07735","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":5,"n_ran_checked":7,"n_instrument":3,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"10 ran (of which 5 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/enhance-the-visual-representation-via#ran","syntology_url":"https://syntology.ai/paper/2209.07735","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.07735"}},"official":{"repos":["alibaba/easyrobust"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":5,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/towards-bridging-the-performance-gaps-of","slug":"towards-bridging-the-performance-gaps-of","title":"Towards Bridging the Performance Gaps of Joint Energy-based Models","date":"2022-09-16","arxiv_id":"2209.07959","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"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) · 4 unverified","sample_list":"/paper/towards-bridging-the-performance-gaps-of#ran","syntology_url":"https://syntology.ai/paper/2209.07959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.07959"}},"official":{"repos":["sndnyang/sadajem"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/visual-recognition-with-deep-nearest","slug":"visual-recognition-with-deep-nearest","title":"Visual Recognition with Deep Nearest Centroids","date":"2022-09-15","arxiv_id":"2209.07383","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/visual-recognition-with-deep-nearest#ran","syntology_url":"https://syntology.ai/paper/2209.07383","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.07383"}},"official":{"repos":["chenghan111/dnc"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/test-time-prompt-tuning-for-zero-shot","slug":"test-time-prompt-tuning-for-zero-shot","title":"Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models","date":"2022-09-15","arxiv_id":"2209.07511","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":2,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/test-time-prompt-tuning-for-zero-shot#ran","syntology_url":"https://syntology.ai/paper/2209.07511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.07511"}},"official":{"repos":["azshue/TPT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pali-a-jointly-scaled-multilingual-language","slug":"pali-a-jointly-scaled-multilingual-language","title":"PaLI: A Jointly-Scaled Multilingual Language-Image Model","date":"2022-09-14","arxiv_id":"2209.06794","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/pali-a-jointly-scaled-multilingual-language#ran","syntology_url":"https://syntology.ai/paper/2209.06794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.06794"}},"official":{"repos":["google-research/big_vision"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/psaq-vit-v2-towards-accurate-and-general-data","slug":"psaq-vit-v2-towards-accurate-and-general-data","title":"PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers","date":"2022-09-13","arxiv_id":"2209.05687","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"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) · 3 unverified","sample_list":"/paper/psaq-vit-v2-towards-accurate-and-general-data#ran","syntology_url":"https://syntology.ai/paper/2209.05687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.05687"}},"official":{"repos":["zkkli/psaq-vit"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/towards-sparsification-of-graph-neural","slug":"towards-sparsification-of-graph-neural","title":"Towards Sparsification of Graph Neural Networks","date":"2022-09-11","arxiv_id":"2209.04766","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/towards-sparsification-of-graph-neural#ran","syntology_url":"https://syntology.ai/paper/2209.04766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.04766"}},"official":{"repos":["harveyp123/iccd_sptrn_slr"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-target-representations-for-masked","slug":"exploring-target-representations-for-masked","title":"Exploring Target Representations for Masked Autoencoders","date":"2022-09-08","arxiv_id":"2209.03917","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/exploring-target-representations-for-masked#ran","syntology_url":"https://syntology.ai/paper/2209.03917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.03917"}},"official":{"repos":["liuxingbin/dbot"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/data-feedback-loops-model-driven","slug":"data-feedback-loops-model-driven","title":"Data Feedback Loops: Model-driven Amplification of Dataset Biases","date":"2022-09-08","arxiv_id":"2209.03942","repositories_listed":1,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/data-feedback-loops-model-driven#ran","syntology_url":"https://syntology.ai/paper/2209.03942","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.03942"}},"official":{"repos":["rtaori/data_feedback"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/proco-prototype-aware-contrastive-learning","slug":"proco-prototype-aware-contrastive-learning","title":"ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification","date":"2022-09-01","arxiv_id":"2209.00183","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":8,"phrase":"6 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; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/proco-prototype-aware-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2209.00183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.00183"}},"official":{"repos":["skyz215/proco"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/noisy-inliers-make-great-outliers-out-of","slug":"noisy-inliers-make-great-outliers-out-of","title":"SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection","date":"2022-08-29","arxiv_id":"2208.13930","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/noisy-inliers-make-great-outliers-out-of#ran","syntology_url":"https://syntology.ai/paper/2208.13930","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.13930"}},"official":{"repos":["samwilso/safe_official"],"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/disentangle-and-remerge-interventional","slug":"disentangle-and-remerge-interventional","title":"Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective","date":"2022-08-26","arxiv_id":"2208.12681","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/disentangle-and-remerge-interventional#ran","syntology_url":"https://syntology.ai/paper/2208.12681","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.12681"}},"official":{"repos":["zyn-1101/dandr"],"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/mixskd-self-knowledge-distillation-from-mixup","slug":"mixskd-self-knowledge-distillation-from-mixup","title":"MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition","date":"2022-08-11","arxiv_id":"2208.05768","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":3,"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 3 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; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/mixskd-self-knowledge-distillation-from-mixup#ran","syntology_url":"https://syntology.ai/paper/2208.05768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.05768"}},"official":{"repos":["winycg/self-kd-lib"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/patching-open-vocabulary-models-by","slug":"patching-open-vocabulary-models-by","title":"Patching open-vocabulary models by interpolating weights","date":"2022-08-10","arxiv_id":"2208.05592","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"5 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/patching-open-vocabulary-models-by#ran","syntology_url":"https://syntology.ai/paper/2208.05592","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.05592"}},"official":{"repos":["mlfoundations/patching"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/almost-orthogonal-layers-for-efficient","slug":"almost-orthogonal-layers-for-efficient","title":"Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks","date":"2022-08-05","arxiv_id":"2208.03160","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":0,"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/almost-orthogonal-layers-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2208.03160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.03160"}},"official":{"repos":["berndprach/aol"],"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"]}}},{"url":"/paper/centrality-and-consistency-two-stage-clean","slug":"centrality-and-consistency-two-stage-clean","title":"Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels","date":"2022-07-29","arxiv_id":"2207.14476","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"6 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/centrality-and-consistency-two-stage-clean#ran","syntology_url":"https://syntology.ai/paper/2207.14476","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.14476"}},"official":{"repos":["uitrbn/tscsi_idn"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/cram-a-compression-aware-minimizer","slug":"cram-a-compression-aware-minimizer","title":"CrAM: A Compression-Aware Minimizer","date":"2022-07-28","arxiv_id":"2207.14200","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/cram-a-compression-aware-minimizer#ran","syntology_url":"https://syntology.ai/paper/2207.14200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.14200"}},"official":{"repos":["ist-daslab/cram"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/hornet-efficient-high-order-spatial","slug":"hornet-efficient-high-order-spatial","title":"HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions","date":"2022-07-28","arxiv_id":"2207.14284","repositories_listed":8,"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/hornet-efficient-high-order-spatial#ran","syntology_url":"https://syntology.ai/paper/2207.14284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.14284"}},"official":{"repos":["raoyongming/hornet"],"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/visual-correspondence-based-explanations","slug":"visual-correspondence-based-explanations","title":"Visual correspondence-based explanations improve AI robustness and human-AI team accuracy","date":"2022-07-26","arxiv_id":"2208.00780","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/visual-correspondence-based-explanations#ran","syntology_url":"https://syntology.ai/paper/2208.00780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.00780"}},"official":{"repos":["anguyen8/visual-correspondence-xai"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/tinyvit-fast-pretraining-distillation-for","slug":"tinyvit-fast-pretraining-distillation-for","title":"TinyViT: Fast Pretraining Distillation for Small Vision Transformers","date":"2022-07-21","arxiv_id":"2207.10666","repositories_listed":3,"syntology":{"n":11,"n_ran":9,"n_constructed":8,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 8 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/tinyvit-fast-pretraining-distillation-for#ran","syntology_url":"https://syntology.ai/paper/2207.10666","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10666"}},"official":{"repos":["microsoft/cream"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":8,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/latent-discriminant-deterministic-uncertainty","slug":"latent-discriminant-deterministic-uncertainty","title":"Latent Discriminant deterministic Uncertainty","date":"2022-07-20","arxiv_id":"2207.10130","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/latent-discriminant-deterministic-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2207.10130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10130"}},"official":{"repos":["ensta-u2is/ldu"],"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/balanced-contrastive-learning-for-long-tailed-1","slug":"balanced-contrastive-learning-for-long-tailed-1","title":"Balanced Contrastive Learning for Long-Tailed Visual Recognition","date":"2022-07-19","arxiv_id":"2207.09052","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/balanced-contrastive-learning-for-long-tailed-1#ran","syntology_url":"https://syntology.ai/paper/2207.09052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09052"}},"official":{"repos":["flamiezhu/bcl"],"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/tree-structure-aware-few-shot-image","slug":"tree-structure-aware-few-shot-image","title":"Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation","date":"2022-07-14","arxiv_id":"2207.06989","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/tree-structure-aware-few-shot-image#ran","syntology_url":"https://syntology.ai/paper/2207.06989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.06989"}},"official":{"repos":["remiMZ/HTS-ECCV22"],"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/contrastive-deep-supervision","slug":"contrastive-deep-supervision","title":"Contrastive Deep Supervision","date":"2022-07-12","arxiv_id":"2207.05306","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"7 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/contrastive-deep-supervision#ran","syntology_url":"https://syntology.ai/paper/2207.05306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.05306"}},"official":{"repos":["archiplab-linfengzhang/contrastive-deep-supervision"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/next-vit-next-generation-vision-transformer","slug":"next-vit-next-generation-vision-transformer","title":"Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios","date":"2022-07-12","arxiv_id":"2207.05501","repositories_listed":5,"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":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) · 1 unverified","sample_list":"/paper/next-vit-next-generation-vision-transformer#ran","syntology_url":"https://syntology.ai/paper/2207.05501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.05501"}},"official":{"repos":["bytedance/next-vit"],"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/lightvit-towards-light-weight-convolution","slug":"lightvit-towards-light-weight-convolution","title":"LightViT: Towards Light-Weight Convolution-Free Vision Transformers","date":"2022-07-12","arxiv_id":"2207.05557","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":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lightvit-towards-light-weight-convolution#ran","syntology_url":"https://syntology.ai/paper/2207.05557","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.05557"}},"official":{"repos":["hunto/lightvit"],"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"]}}}],"record_sha256":"559e621ef0051adccdd59383532121dd88b5c73364cf7690a9b90bc03b810ff9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}