{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/representation-learning/papers/16","list_of":"/task/representation-learning","task":"Representation Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":16,"pages_in_order":106,"rows_per_page":100,"rows":[1501,1600],"of":10580,"counts":{"archive_papers_tagged":10580,"with_a_code_link":4662,"where_syntology_ran_a_sample":1439,"not_listed_spam_title":0,"listed":10580,"listed_where_code_ran":1439,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1228,"every_run_a_failure_of_syntologys_instrument":211,"listed_with_a_run_with_no_instrument_failure":1228,"listed_every_run_a_failure_of_syntologys_instrument":211,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/representation-learning","prev":"/task/representation-learning/papers/15","next":"/task/representation-learning/papers/17","papers":[{"url":"/paper/dacad-domain-adaptation-contrastive-learning","slug":"dacad-domain-adaptation-contrastive-learning","title":"DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series","date":"2024-04-17","arxiv_id":"2404.11269","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dacad-domain-adaptation-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2404.11269","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.11269"}},"official":{"repos":["zamanzadeh/DACAD"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cluster-based-graph-collaborative-filtering","slug":"cluster-based-graph-collaborative-filtering","title":"Cluster-based Graph Collaborative Filtering","date":"2024-04-16","arxiv_id":"2404.10321","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cluster-based-graph-collaborative-filtering#ran","syntology_url":"https://syntology.ai/paper/2404.10321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10321"}},"official":{"repos":["zhao254014/clustergcf"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-self-adaptive-multiscale-distillation","slug":"dynamic-self-adaptive-multiscale-distillation","title":"Dynamic Self-adaptive Multiscale Distillation from Pre-trained Multimodal Large Model for Efficient Cross-modal Representation Learning","date":"2024-04-16","arxiv_id":"2404.10838","repositories_listed":1,"syntology":null},{"url":"/paper/tripod-three-complementary-inductive-biases","slug":"tripod-three-complementary-inductive-biases","title":"Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning","date":"2024-04-16","arxiv_id":"2404.10282","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/tripod-three-complementary-inductive-biases#ran","syntology_url":"https://syntology.ai/paper/2404.10282","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10282"}},"official":{"repos":["kylehkhsu/tripod"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-pretraining-for-visual-concept","slug":"contrastive-pretraining-for-visual-concept","title":"Contrastive Pretraining for Visual Concept Explanations of Socioeconomic Outcomes","date":"2024-04-15","arxiv_id":"2404.09768","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-self-training-aligning-images-and","slug":"cross-modal-self-training-aligning-images-and","title":"Cross-Modal Self-Training: Aligning Images and Pointclouds to Learn Classification without Labels","date":"2024-04-15","arxiv_id":"2404.10146","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-enhanced-visual-language","slug":"knowledge-enhanced-visual-language","title":"Knowledge-enhanced Visual-Language Pretraining for Computational Pathology","date":"2024-04-15","arxiv_id":"2404.09942","repositories_listed":1,"syntology":null},{"url":"/paper/neuro-inspired-information-theoretic","slug":"neuro-inspired-information-theoretic","title":"Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning","date":"2024-04-15","arxiv_id":"2404.09403","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/neuro-inspired-information-theoretic#ran","syntology_url":"https://syntology.ai/paper/2404.09403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.09403"}},"official":{"repos":["joshuaxiao98/ithp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/videosage-video-summarization-with-graph","slug":"videosage-video-summarization-with-graph","title":"VideoSAGE: Video Summarization with Graph Representation Learning","date":"2024-04-14","arxiv_id":"2404.10539","repositories_listed":1,"syntology":null},{"url":"/paper/masked-image-modeling-as-a-framework-for-self","slug":"masked-image-modeling-as-a-framework-for-self","title":"Masked Image Modeling as a Framework for Self-Supervised Learning across Eye Movements","date":"2024-04-12","arxiv_id":"2404.08526","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-fair-representation-learning-for","slug":"adaptive-fair-representation-learning-for","title":"Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment","date":"2024-04-11","arxiv_id":"2404.07494","repositories_listed":1,"syntology":null},{"url":"/paper/connecting-nerfs-images-and-text","slug":"connecting-nerfs-images-and-text","title":"Connecting NeRFs, Images, and Text","date":"2024-04-11","arxiv_id":"2404.07993","repositories_listed":1,"syntology":null},{"url":"/paper/mindbridge-a-cross-subject-brain-decoding","slug":"mindbridge-a-cross-subject-brain-decoding","title":"MindBridge: A Cross-Subject Brain Decoding Framework","date":"2024-04-11","arxiv_id":"2404.07850","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":3,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mindbridge-a-cross-subject-brain-decoding#ran","syntology_url":"https://syntology.ai/paper/2404.07850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07850"}},"official":{"repos":["littlepure2333/mindbridge"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/representation-learning-of-tangled-key-value","slug":"representation-learning-of-tangled-key-value","title":"Representation Learning of Tangled Key-Value Sequence Data for Early Classification","date":"2024-04-11","arxiv_id":"2404.07454","repositories_listed":1,"syntology":null},{"url":"/paper/two-effects-one-trigger-on-the-modality-gap","slug":"two-effects-one-trigger-on-the-modality-gap","title":"Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models","date":"2024-04-11","arxiv_id":"2404.07983","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/two-effects-one-trigger-on-the-modality-gap#ran","syntology_url":"https://syntology.ai/paper/2404.07983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07983"}},"official":{"repos":["lmb-freiburg/two-effects-one-trigger"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/advancing-real-time-pandemic-forecasting","slug":"advancing-real-time-pandemic-forecasting","title":"Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study","date":"2024-04-10","arxiv_id":"2404.06962","repositories_listed":1,"syntology":null},{"url":"/paper/unified-language-driven-zero-shot-domain","slug":"unified-language-driven-zero-shot-domain","title":"Unified Language-driven Zero-shot Domain Adaptation","date":"2024-04-10","arxiv_id":"2404.07155","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unified-language-driven-zero-shot-domain#ran","syntology_url":"https://syntology.ai/paper/2404.07155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07155"}},"official":{"repos":["Yangsenqiao/ULDA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/actnetformer-transformer-resnet-hybrid-method","slug":"actnetformer-transformer-resnet-hybrid-method","title":"ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos","date":"2024-04-09","arxiv_id":"2404.06243","repositories_listed":1,"syntology":null},{"url":"/paper/vi-ood-a-unified-representation-learning","slug":"vi-ood-a-unified-representation-learning","title":"VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection","date":"2024-04-09","arxiv_id":"2404.06217","repositories_listed":1,"syntology":null},{"url":"/paper/frequency-decomposition-driven-unsupervised","slug":"frequency-decomposition-driven-unsupervised","title":"Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation","date":"2024-04-06","arxiv_id":"2404.04531","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/frequency-decomposition-driven-unsupervised#ran","syntology_url":"https://syntology.ai/paper/2404.04531","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04531"}},"official":{"repos":["sstary/ssrs"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-exploring-pde-modeling-for-point-cloud","slug":"on-exploring-pde-modeling-for-point-cloud","title":"On Exploring PDE Modeling for Point Cloud Video Representation Learning","date":"2024-04-06","arxiv_id":"2404.04720","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-exploring-pde-modeling-for-point-cloud#ran","syntology_url":"https://syntology.ai/paper/2404.04720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04720"}},"official":{"repos":["zhh6425/MotionPointNet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/soft-prompting-with-graph-of-thought-for","slug":"soft-prompting-with-graph-of-thought-for","title":"Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning","date":"2024-04-06","arxiv_id":"2404.04538","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/soft-prompting-with-graph-of-thought-for#ran","syntology_url":"https://syntology.ai/paper/2404.04538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04538"}},"official":{"repos":["shishicode/agot"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dwell-in-the-beginning-how-language-models","slug":"dwell-in-the-beginning-how-language-models","title":"Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval","date":"2024-04-05","arxiv_id":"2404.04163","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":10,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dwell-in-the-beginning-how-language-models#ran","syntology_url":"https://syntology.ai/paper/2404.04163","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04163"}},"official":{"repos":["cxcscmu/longembeddinganalysis"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bishop-bi-directional-cellular-learning-for","slug":"bishop-bi-directional-cellular-learning-for","title":"BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model","date":"2024-04-04","arxiv_id":"2404.03830","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bishop-bi-directional-cellular-learning-for#ran","syntology_url":"https://syntology.ai/paper/2404.03830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03830"}},"official":{"repos":["magics-lab/bishop"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unveiling-llms-the-evolution-of-latent","slug":"unveiling-llms-the-evolution-of-latent","title":"Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph","date":"2024-04-04","arxiv_id":"2404.03623","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unveiling-llms-the-evolution-of-latent#ran","syntology_url":"https://syntology.ai/paper/2404.03623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03623"}},"official":{"repos":["Ipazia-AI/latent-explorer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/masked-completion-via-structured-diffusion","slug":"masked-completion-via-structured-diffusion","title":"Masked Completion via Structured Diffusion with White-Box Transformers","date":"2024-04-03","arxiv_id":"2404.02446","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/masked-completion-via-structured-diffusion#ran","syntology_url":"https://syntology.ai/paper/2404.02446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02446"}},"official":{"repos":["ma-lab-berkeley/crate"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/contrastcad-contrastive-learning-based","slug":"contrastcad-contrastive-learning-based","title":"ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Models","date":"2024-04-02","arxiv_id":"2404.01645","repositories_listed":1,"syntology":null},{"url":"/paper/propensity-score-alignment-of-unpaired","slug":"propensity-score-alignment-of-unpaired","title":"Propensity Score Alignment of Unpaired Multimodal Data","date":"2024-04-02","arxiv_id":"2404.01595","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":5,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","sample_list":"/paper/propensity-score-alignment-of-unpaired#ran","syntology_url":"https://syntology.ai/paper/2404.01595","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01595"}},"official":{"repos":["valence-labs/prop-score-pairing"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/universal-representations-for-financial","slug":"universal-representations-for-financial","title":"Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge","date":"2024-04-02","arxiv_id":"2404.02047","repositories_listed":1,"syntology":null},{"url":"/paper/nerf-mae-masked-autoencoders-for-self","slug":"nerf-mae-masked-autoencoders-for-self","title":"NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields","date":"2024-04-01","arxiv_id":"2404.01300","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/nerf-mae-masked-autoencoders-for-self#ran","syntology_url":"https://syntology.ai/paper/2404.01300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01300"}},"official":{"repos":["zubair-irshad/NeRF-MAE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/structured-information-matters-incorporating","slug":"structured-information-matters-incorporating","title":"Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation","date":"2024-04-01","arxiv_id":"2404.01129","repositories_listed":1,"syntology":null},{"url":"/paper/addressing-loss-of-plasticity-and","slug":"addressing-loss-of-plasticity-and","title":"Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning","date":"2024-03-31","arxiv_id":"2404.00781","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/addressing-loss-of-plasticity-and#ran","syntology_url":"https://syntology.ai/paper/2404.00781","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00781"}},"official":{"repos":["mohmdelsayed/upgd"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hypeboy-generative-self-supervised","slug":"hypeboy-generative-self-supervised","title":"HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs","date":"2024-03-31","arxiv_id":"2404.00638","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hypeboy-generative-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2404.00638","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00638"}},"official":{"repos":["kswoo97/hypeboy"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/clustering-for-protein-representation","slug":"clustering-for-protein-representation","title":"Clustering for Protein Representation Learning","date":"2024-03-30","arxiv_id":"2404.00254","repositories_listed":1,"syntology":null},{"url":"/paper/geoauxnet-towards-universal-3d-representation","slug":"geoauxnet-towards-universal-3d-representation","title":"GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds","date":"2024-03-28","arxiv_id":"2403.19220","repositories_listed":1,"syntology":null},{"url":"/paper/mpxgat-an-attention-based-deep-learning-model","slug":"mpxgat-an-attention-based-deep-learning-model","title":"MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding","date":"2024-03-28","arxiv_id":"2403.19246","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-embeddings-the-promise-of-visual-table","slug":"beyond-embeddings-the-promise-of-visual-table","title":"Beyond Embeddings: The Promise of Visual Table in Visual Reasoning","date":"2024-03-27","arxiv_id":"2403.18252","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":3,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 2 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/beyond-embeddings-the-promise-of-visual-table#ran","syntology_url":"https://syntology.ai/paper/2403.18252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18252"}},"official":{"repos":["lavi-lab/visual-table"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/digital-audio-tampering-detection-based-on-1","slug":"digital-audio-tampering-detection-based-on-1","title":"Digital audio tampering detection based on spatio-temporal representation learning of electrical network frequency.","date":"2024-03-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/equipping-sketch-patches-with-context-aware","slug":"equipping-sketch-patches-with-context-aware","title":"Equipping Sketch Patches with Context-Aware Positional Encoding for Graphic Sketch Representation","date":"2024-03-26","arxiv_id":"2403.17525","repositories_listed":1,"syntology":null},{"url":"/paper/grad-camo-learning-interpretable-single-cell","slug":"grad-camo-learning-interpretable-single-cell","title":"Grad-CAMO: Learning Interpretable Single-Cell Morphological Profiles from 3D Cell Painting Images","date":"2024-03-26","arxiv_id":"2403.17615","repositories_listed":1,"syntology":null},{"url":"/paper/hill-hierarchy-aware-information-lossless","slug":"hill-hierarchy-aware-information-lossless","title":"HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification","date":"2024-03-26","arxiv_id":"2403.17307","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hill-hierarchy-aware-information-lossless#ran","syntology_url":"https://syntology.ai/paper/2403.17307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17307"}},"official":{"repos":["rooooyy/hill"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-clustering-based-visual-representation","slug":"neural-clustering-based-visual-representation","title":"Neural Clustering based Visual Representation Learning","date":"2024-03-26","arxiv_id":"2403.17409","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/neural-clustering-based-visual-representation#ran","syntology_url":"https://syntology.ai/paper/2403.17409","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17409"}},"official":{"repos":["guikunchen/fec"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sghormer-an-energy-saving-graph-transformer","slug":"sghormer-an-energy-saving-graph-transformer","title":"SGHormer: An Energy-Saving Graph Transformer Driven by Spikes","date":"2024-03-26","arxiv_id":"2403.17656","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-information-extraction-in-few-shot","slug":"efficient-information-extraction-in-few-shot","title":"Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning","date":"2024-03-25","arxiv_id":"2403.16543","repositories_listed":1,"syntology":null},{"url":"/paper/ualign-pushing-the-limit-of-template-free","slug":"ualign-pushing-the-limit-of-template-free","title":"UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment","date":"2024-03-25","arxiv_id":"2404.00044","repositories_listed":1,"syntology":null},{"url":"/paper/omni-kernel-network-for-image-restoration","slug":"omni-kernel-network-for-image-restoration","title":"Omni-Kernel Network for Image Restoration","date":"2024-03-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/investigating-similarities-across","slug":"investigating-similarities-across","title":"Investigating Similarities Across Decentralized Financial (DeFi) Services","date":"2024-03-23","arxiv_id":"2404.00034","repositories_listed":1,"syntology":null},{"url":"/paper/gtc-gnn-transformer-co-contrastive-learning","slug":"gtc-gnn-transformer-co-contrastive-learning","title":"GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation","date":"2024-03-22","arxiv_id":"2403.15520","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/gtc-gnn-transformer-co-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2403.15520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.15520"}},"official":{"repos":["phd-lanyu/gtc"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/trajectory-regularization-enhances-self","slug":"trajectory-regularization-enhances-self","title":"Pose-Aware Self-Supervised Learning with Viewpoint Trajectory Regularization","date":"2024-03-22","arxiv_id":"2403.14973","repositories_listed":1,"syntology":null},{"url":"/paper/a-classifier-based-approach-to-multi-class","slug":"a-classifier-based-approach-to-multi-class","title":"A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients","date":"2024-03-21","arxiv_id":"2403.14742","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-balancing-representation-learning","slug":"contrastive-balancing-representation-learning","title":"Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation","date":"2024-03-21","arxiv_id":"2403.14232","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":4,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"7 ran (of which 4 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/contrastive-balancing-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2403.14232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14232"}},"official":{"repos":["euzmin/Contrastive-Balancing-Representation-Network-CRNet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":4,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hierarchical-text-to-vision-self-supervised","slug":"hierarchical-text-to-vision-self-supervised","title":"Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning","date":"2024-03-21","arxiv_id":"2403.14616","repositories_listed":1,"syntology":null},{"url":"/paper/m3-a-multi-task-mixed-objective-learning","slug":"m3-a-multi-task-mixed-objective-learning","title":"M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence Retrieval","date":"2024-03-21","arxiv_id":"2403.14074","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-temporal-graph-representation","slug":"spatial-temporal-graph-representation","title":"Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction","date":"2024-03-20","arxiv_id":"2403.13872","repositories_listed":1,"syntology":null},{"url":"/paper/towards-principled-representation-learning-1","slug":"towards-principled-representation-learning-1","title":"Towards Principled Representation Learning from Videos for Reinforcement Learning","date":"2024-03-20","arxiv_id":"2403.13765","repositories_listed":1,"syntology":null},{"url":"/paper/dmad-dual-memory-bank-for-real-world-anomaly","slug":"dmad-dual-memory-bank-for-real-world-anomaly","title":"DMAD: Dual Memory Bank for Real-World Anomaly Detection","date":"2024-03-19","arxiv_id":"2403.12362","repositories_listed":1,"syntology":null},{"url":"/paper/do-generated-data-always-help-contrastive","slug":"do-generated-data-always-help-contrastive","title":"Do Generated Data Always Help Contrastive Learning?","date":"2024-03-19","arxiv_id":"2403.12448","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/do-generated-data-always-help-contrastive#ran","syntology_url":"https://syntology.ai/paper/2403.12448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12448"}},"official":{"repos":["pku-ml/adainf"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/eye-gaze-guided-multi-modal-alignment","slug":"eye-gaze-guided-multi-modal-alignment","title":"Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning","date":"2024-03-19","arxiv_id":"2403.12416","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/eye-gaze-guided-multi-modal-alignment#ran","syntology_url":"https://syntology.ai/paper/2403.12416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12416"}},"official":{"repos":["momarky/egma"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/flowerformer-empowering-neural-architecture","slug":"flowerformer-empowering-neural-architecture","title":"FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer","date":"2024-03-19","arxiv_id":"2403.12821","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/flowerformer-empowering-neural-architecture#ran","syntology_url":"https://syntology.ai/paper/2403.12821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12821"}},"official":{"repos":["y0ngjaenius/cvpr2024_flowerformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/complete-and-efficient-graph-transformers-for","slug":"complete-and-efficient-graph-transformers-for","title":"Complete and Efficient Graph Transformers for Crystal Material Property Prediction","date":"2024-03-18","arxiv_id":"2403.11857","repositories_listed":1,"syntology":null},{"url":"/paper/hvdistill-transferring-knowledge-from-images","slug":"hvdistill-transferring-knowledge-from-images","title":"HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation","date":"2024-03-18","arxiv_id":"2403.11817","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hvdistill-transferring-knowledge-from-images#ran","syntology_url":"https://syntology.ai/paper/2403.11817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11817"}},"official":{"repos":["zhangsha1024/HVDistill"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ipcl-iterative-pseudo-supervised-contrastive","slug":"ipcl-iterative-pseudo-supervised-contrastive","title":"IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature Representation","date":"2024-03-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-useful-representations-of-recurrent","slug":"learning-useful-representations-of-recurrent","title":"Learning Useful Representations of Recurrent Neural Network Weight Matrices","date":"2024-03-18","arxiv_id":"2403.11998","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-useful-representations-of-recurrent#ran","syntology_url":"https://syntology.ai/paper/2403.11998","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11998"}},"official":{"repos":["vincentherrmann/rnn-weights-representation-learning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/relational-representation-learning-network","slug":"relational-representation-learning-network","title":"Relational Representation Learning Network for Cross-Spectral Image Patch Matching","date":"2024-03-18","arxiv_id":"2403.11751","repositories_listed":1,"syntology":null},{"url":"/paper/entity-alignment-with-unlabeled-dangling","slug":"entity-alignment-with-unlabeled-dangling","title":"Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases","date":"2024-03-16","arxiv_id":"2403.10978","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/entity-alignment-with-unlabeled-dangling#ran","syntology_url":"https://syntology.ai/paper/2403.10978","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10978"}},"official":{"repos":["Handon112358/NeurIPS_2024_Learning-Matchable-Prior-For-Entity-Alignment-with-Unlabeled-Dangling-Cases"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-multi-view-representation-learning","slug":"rethinking-multi-view-representation-learning","title":"Rethinking Multi-view Representation Learning via Distilled Disentangling","date":"2024-03-16","arxiv_id":"2403.10897","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":7,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"8 ran (of which 7 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/rethinking-multi-view-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2403.10897","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10897"}},"official":{"repos":["guanzhou-ke/mrdd"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/time-series-representation-learning-with","slug":"time-series-representation-learning-with","title":"Time Series Representation Learning with Supervised Contrastive Temporal Transformer","date":"2024-03-16","arxiv_id":"2403.10787","repositories_listed":1,"syntology":null},{"url":"/paper/coreecho-continuous-representation-learning","slug":"coreecho-continuous-representation-learning","title":"CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis","date":"2024-03-15","arxiv_id":"2403.10164","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/coreecho-continuous-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2403.10164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10164"}},"official":{"repos":["biomedia-mbzuai/coreecho"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/medslip-medical-dual-stream-language-image","slug":"medslip-medical-dual-stream-language-image","title":"MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained Alignment","date":"2024-03-15","arxiv_id":"2403.10635","repositories_listed":1,"syntology":null},{"url":"/paper/t4p-test-time-training-of-trajectory","slug":"t4p-test-time-training-of-trajectory","title":"T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory","date":"2024-03-15","arxiv_id":"2403.10052","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/t4p-test-time-training-of-trajectory#ran","syntology_url":"https://syntology.ai/paper/2403.10052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10052"}},"official":{"repos":["daeheepark/t4p"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/equiav-leveraging-equivariance-for-audio","slug":"equiav-leveraging-equivariance-for-audio","title":"EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning","date":"2024-03-14","arxiv_id":"2403.09502","repositories_listed":1,"syntology":{"n":13,"n_ran":6,"n_constructed":4,"n_ran_checked":6,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/equiav-leveraging-equivariance-for-audio#ran","syntology_url":"https://syntology.ai/paper/2403.09502","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09502"}},"official":{"repos":["jongsuk1/equiav"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/groupcontrast-semantic-aware-self-supervised","slug":"groupcontrast-semantic-aware-self-supervised","title":"GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding","date":"2024-03-14","arxiv_id":"2403.09639","repositories_listed":1,"syntology":null},{"url":"/paper/hyper-cl-conditioning-sentence","slug":"hyper-cl-conditioning-sentence","title":"Hyper-CL: Conditioning Sentence Representations with Hypernetworks","date":"2024-03-14","arxiv_id":"2403.09490","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hyper-cl-conditioning-sentence#ran","syntology_url":"https://syntology.ai/paper/2403.09490","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09490"}},"official":{"repos":["hyu-nlp/hyper-cl"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-learning-for-time-series-1","slug":"self-supervised-learning-for-time-series-1","title":"Self-Supervised Learning for Time Series: Contrastive or Generative?","date":"2024-03-14","arxiv_id":"2403.09809","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-learning-for-time-series-1#ran","syntology_url":"https://syntology.ai/paper/2403.09809","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09809"}},"official":{"repos":["dl4mhealth/ssl_comparison"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-sparsity-principle-for-partially-observable","slug":"a-sparsity-principle-for-partially-observable","title":"A Sparsity Principle for Partially Observable Causal Representation Learning","date":"2024-03-13","arxiv_id":"2403.08335","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-sparsity-principle-for-partially-observable#ran","syntology_url":"https://syntology.ai/paper/2403.08335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.08335"}},"official":{"repos":["danrux/sparsity-crl"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/an-efficient-end-to-end-approach-to-noise","slug":"an-efficient-end-to-end-approach-to-noise","title":"An Efficient End-to-End Approach to Noise Invariant Speech Features via Multi-Task Learning","date":"2024-03-13","arxiv_id":"2403.08654","repositories_listed":1,"syntology":null},{"url":"/paper/focusmae-gallbladder-cancer-detection-from","slug":"focusmae-gallbladder-cancer-detection-from","title":"FocusMAE: Gallbladder Cancer Detection from Ultrasound Videos with Focused Masked Autoencoders","date":"2024-03-13","arxiv_id":"2403.08848","repositories_listed":1,"syntology":null},{"url":"/paper/mim4d-masked-modeling-with-multi-view-video","slug":"mim4d-masked-modeling-with-multi-view-video","title":"MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation Learning","date":"2024-03-13","arxiv_id":"2403.08760","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-policy-from-offline-task","slug":"disentangling-policy-from-offline-task","title":"Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation","date":"2024-03-12","arxiv_id":"2403.07261","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-graph-representation-with-knowledge","slug":"dynamic-graph-representation-with-knowledge","title":"Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis","date":"2024-03-12","arxiv_id":"2403.07719","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dynamic-graph-representation-with-knowledge#ran","syntology_url":"https://syntology.ai/paper/2403.07719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07719"}},"official":{"repos":["wonderlandxd/wikg"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/spatiotemporal-representation-learning-for","slug":"spatiotemporal-representation-learning-for","title":"Spatiotemporal Representation Learning for Short and Long Medical Image Time Series","date":"2024-03-12","arxiv_id":"2403.07513","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-latent-graph-representations-of","slug":"optimizing-latent-graph-representations-of","title":"Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot Domain Transfer","date":"2024-03-11","arxiv_id":"2403.06953","repositories_listed":1,"syntology":null},{"url":"/paper/see-through-their-minds-learning-transferable","slug":"see-through-their-minds-learning-transferable","title":"See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRI","date":"2024-03-11","arxiv_id":"2403.06361","repositories_listed":1,"syntology":null},{"url":"/paper/the-power-of-noise-toward-a-unified-multi","slug":"the-power-of-noise-toward-a-unified-multi","title":"Noise-powered Multi-modal Knowledge Graph Representation Framework","date":"2024-03-11","arxiv_id":"2403.06832","repositories_listed":1,"syntology":null},{"url":"/paper/decoupled-contrastive-learning-for-long","slug":"decoupled-contrastive-learning-for-long","title":"Decoupled Contrastive Learning for Long-Tailed Recognition","date":"2024-03-10","arxiv_id":"2403.06151","repositories_listed":1,"syntology":null},{"url":"/paper/pepsi-pathology-enhanced-pulse-sequence","slug":"pepsi-pathology-enhanced-pulse-sequence","title":"PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI","date":"2024-03-10","arxiv_id":"2403.06227","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/pepsi-pathology-enhanced-pulse-sequence#ran","syntology_url":"https://syntology.ai/paper/2403.06227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06227"}},"official":{"repos":["peirong26/PEPSI"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/predicting-single-cell-drug-sensitivity-by","slug":"predicting-single-cell-drug-sensitivity-by","title":"Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learning","date":"2024-03-08","arxiv_id":"2403.05260","repositories_listed":1,"syntology":null},{"url":"/paper/tracing-the-roots-of-facts-in-multilingual","slug":"tracing-the-roots-of-facts-in-multilingual","title":"Tracing the Roots of Facts in Multilingual Language Models: Independent, Shared, and Transferred Knowledge","date":"2024-03-08","arxiv_id":"2403.05189","repositories_listed":1,"syntology":null},{"url":"/paper/context-based-multimodal-fusion","slug":"context-based-multimodal-fusion","title":"Lightweight Cross-Modal Representation Learning","date":"2024-03-07","arxiv_id":"2403.04650","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-continual-learning-with","slug":"contrastive-continual-learning-with","title":"Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation","date":"2024-03-07","arxiv_id":"2403.04599","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/contrastive-continual-learning-with#ran","syntology_url":"https://syntology.ai/paper/2403.04599","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.04599"}},"official":{"repos":["lijy373/cclis"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-of-encoder-based-warm-start","slug":"rethinking-of-encoder-based-warm-start","title":"Rethinking of Encoder-based Warm-start Methods in Hyperparameter Optimization","date":"2024-03-07","arxiv_id":"2403.04720","repositories_listed":1,"syntology":null},{"url":"/paper/sdpl-shifting-dense-partition-learning-for","slug":"sdpl-shifting-dense-partition-learning-for","title":"SDPL: Shifting-Dense Partition Learning for UAV-View Geo-Localization","date":"2024-03-07","arxiv_id":"2403.04172","repositories_listed":1,"syntology":null},{"url":"/paper/self-attention-empowered-graph-convolutional","slug":"self-attention-empowered-graph-convolutional","title":"Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding","date":"2024-03-06","arxiv_id":"2403.03465","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-photographic-image-layout","slug":"self-supervised-photographic-image-layout","title":"Self-supervised Photographic Image Layout Representation Learning","date":"2024-03-06","arxiv_id":"2403.03740","repositories_listed":1,"syntology":null},{"url":"/paper/fedhcdr-federated-cross-domain-recommendation","slug":"fedhcdr-federated-cross-domain-recommendation","title":"FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling","date":"2024-03-05","arxiv_id":"2403.02630","repositories_listed":1,"syntology":null},{"url":"/paper/hegae-ac-heterogeneous-graph-auto-encoder-for","slug":"hegae-ac-heterogeneous-graph-auto-encoder-for","title":"HeGAE-AC: heterogeneous graph auto-encoder for attribute completion","date":"2024-03-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/differentially-private-representation","slug":"differentially-private-representation","title":"Differentially Private Representation Learning via Image Captioning","date":"2024-03-04","arxiv_id":"2403.02506","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/differentially-private-representation#ran","syntology_url":"https://syntology.ai/paper/2403.02506","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.02506"}},"official":{"repos":["facebookresearch/dpcap"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/inf2guard-an-information-theoretic-framework","slug":"inf2guard-an-information-theoretic-framework","title":"Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks","date":"2024-03-04","arxiv_id":"2403.02116","repositories_listed":1,"syntology":null},{"url":"/paper/multi-hop-attention-based-graph-pooling-a","slug":"multi-hop-attention-based-graph-pooling-a","title":"Multi-hop Attention-based Graph Pooling: A Personalized PageRank Perspective","date":"2024-03-04","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/decoupling-weighing-and-selecting-for","slug":"decoupling-weighing-and-selecting-for","title":"Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks","date":"2024-03-03","arxiv_id":"2403.01400","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/decoupling-weighing-and-selecting-for#ran","syntology_url":"https://syntology.ai/paper/2403.01400","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01400"}},"official":{"repos":["tianyufan0504/was"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}}],"record_sha256":"ba99456b05af699bf981cfd90067840d87f05841bf0226f915c3f0c38614f7aa","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}