{"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/36","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":36,"pages_in_order":106,"rows_per_page":100,"rows":[3501,3600],"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/35","next":"/task/representation-learning/papers/37","papers":[{"url":"/paper/jointly-learnable-data-augmentations-for-self","slug":"jointly-learnable-data-augmentations-for-self","title":"Jointly Learnable Data Augmentations for Self-Supervised GNNs","date":"2021-08-23","arxiv_id":"2108.10420","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-local-discrimination-for-medical","slug":"unsupervised-local-discrimination-for-medical","title":"Unsupervised Local Discrimination for Medical Images","date":"2021-08-21","arxiv_id":"2108.09440","repositories_listed":1,"syntology":null},{"url":"/paper/geometry-aware-self-training-for-unsupervised","slug":"geometry-aware-self-training-for-unsupervised","title":"Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds","date":"2021-08-20","arxiv_id":"2108.09169","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/geometry-aware-self-training-for-unsupervised#ran","syntology_url":"https://syntology.ai/paper/2108.09169","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.09169"}},"official":{"repos":["zou-longkun/gast"],"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/desyr-definition-and-syntactic-representation","slug":"desyr-definition-and-syntactic-representation","title":"DESYR: Definition and Syntactic Representation Based Claim Detection on the Web","date":"2021-08-19","arxiv_id":"2108.08759","repositories_listed":1,"syntology":null},{"url":"/paper/mr-tydi-a-multi-lingual-benchmark-for-dense","slug":"mr-tydi-a-multi-lingual-benchmark-for-dense","title":"Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval","date":"2021-08-19","arxiv_id":"2108.08787","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/mr-tydi-a-multi-lingual-benchmark-for-dense#ran","syntology_url":"https://syntology.ai/paper/2108.08787","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.08787"}},"official":{"repos":["castorini/mr.tydi"],"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/temporal-graph-network-embedding-with-causal","slug":"temporal-graph-network-embedding-with-causal","title":"Temporal Graph Network Embedding with Causal Anonymous Walks Representations","date":"2021-08-19","arxiv_id":"2108.08754","repositories_listed":1,"syntology":null},{"url":"/paper/learning-by-aligning-visible-infrared-person","slug":"learning-by-aligning-visible-infrared-person","title":"Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences","date":"2021-08-17","arxiv_id":"2108.07422","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/learning-by-aligning-visible-infrared-person#ran","syntology_url":"https://syntology.ai/paper/2108.07422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07422"}},"official":null}},{"url":"/paper/collaborative-unsupervised-visual","slug":"collaborative-unsupervised-visual","title":"Collaborative Unsupervised Visual Representation Learning from Decentralized Data","date":"2021-08-14","arxiv_id":"2108.06492","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 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) · 3 unverified","sample_list":"/paper/collaborative-unsupervised-visual#ran","syntology_url":"https://syntology.ai/paper/2108.06492","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.06492"}},"official":{"repos":["EasyFL-AI/EasyFL"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/heterogeneous-temporal-graph-transformer-an","slug":"heterogeneous-temporal-graph-transformer-an","title":"heterogeneous temporal graph transformer: an intelligent system for evolving android malware detection","date":"2021-08-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-disentanglement-without","slug":"unsupervised-disentanglement-without","title":"Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions","date":"2021-08-14","arxiv_id":"2108.06613","repositories_listed":1,"syntology":null},{"url":"/paper/graph-trend-networks-for-recommendations","slug":"graph-trend-networks-for-recommendations","title":"Graph Trend Filtering Networks for Recommendations","date":"2021-08-12","arxiv_id":"2108.05552","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/graph-trend-networks-for-recommendations#ran","syntology_url":"https://syntology.ai/paper/2108.05552","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05552"}},"official":{"repos":["wenqifan03/gtn-sigir2022"],"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/hopfe-knowledge-graph-representation-learning","slug":"hopfe-knowledge-graph-representation-learning","title":"HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations","date":"2021-08-12","arxiv_id":"2108.05774","repositories_listed":1,"syntology":null},{"url":"/paper/learning-deep-multimodal-feature","slug":"learning-deep-multimodal-feature","title":"Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion","date":"2021-08-11","arxiv_id":"2108.05009","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/learning-deep-multimodal-feature#ran","syntology_url":"https://syntology.ai/paper/2108.05009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05009"}},"official":{"repos":["yikaiw/AsymFusion"],"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/representation-learning-for-remote-sensing-an","slug":"representation-learning-for-remote-sensing-an","title":"Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach","date":"2021-08-11","arxiv_id":"2108.05094","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/representation-learning-for-remote-sensing-an#ran","syntology_url":"https://syntology.ai/paper/2108.05094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05094"}},"official":{"repos":["descarteslabs/contrastive_sensor_fusion"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-consensus-representation","slug":"self-supervised-consensus-representation","title":"Self-supervised Consensus Representation Learning for Attributed Graph","date":"2021-08-10","arxiv_id":"2108.04822","repositories_listed":1,"syntology":null},{"url":"/paper/encoding-heterogeneous-social-and-political","slug":"encoding-heterogeneous-social-and-political","title":"Legislator Representation Learning with Social Context and Expert Knowledge","date":"2021-08-09","arxiv_id":"2108.03881","repositories_listed":1,"syntology":null},{"url":"/paper/towards-to-robust-and-generalized-medical","slug":"towards-to-robust-and-generalized-medical","title":"Towards to Robust and Generalized Medical Image Segmentation Framework","date":"2021-08-09","arxiv_id":"2108.03823","repositories_listed":1,"syntology":null},{"url":"/paper/skeleton-contrastive-3d-action-representation","slug":"skeleton-contrastive-3d-action-representation","title":"Skeleton-Contrastive 3D Action Representation Learning","date":"2021-08-08","arxiv_id":"2108.03656","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":7,"phrase":"4 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/skeleton-contrastive-3d-action-representation#ran","syntology_url":"https://syntology.ai/paper/2108.03656","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.03656"}},"official":{"repos":["fmthoker/skeleton-contrast"],"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","unlocated"]}}},{"url":"/paper/unifying-heterogenous-electronic-health","slug":"unifying-heterogenous-electronic-health","title":"Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding","date":"2021-08-08","arxiv_id":"2108.03625","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-label-aware-graph-convolutional","slug":"adaptive-label-aware-graph-convolutional","title":"Adaptive label-aware graph convolutional networks for cross-modal retrieval","date":"2021-08-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/video-contrastive-learning-with-global","slug":"video-contrastive-learning-with-global","title":"Video Contrastive Learning with Global Context","date":"2021-08-05","arxiv_id":"2108.02722","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/video-contrastive-learning-with-global#ran","syntology_url":"https://syntology.ai/paper/2108.02722","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.02722"}},"official":{"repos":["amazon-research/video-contrastive-learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-self-supervised-video","slug":"enhancing-self-supervised-video","title":"Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization","date":"2021-08-04","arxiv_id":"2108.02183","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":4,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/enhancing-self-supervised-video#ran","syntology_url":"https://syntology.ai/paper/2108.02183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.02183"}},"official":{"repos":["shvdiwnkozbw/video-representation-via-multi-level-optimization"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/hyperparameter-free-and-explainable-whole","slug":"hyperparameter-free-and-explainable-whole","title":"Hyperparameter-free and Explainable Whole Graph Embedding","date":"2021-08-04","arxiv_id":"2108.02113","repositories_listed":1,"syntology":null},{"url":"/paper/semi-weakly-supervised-contrastive","slug":"semi-weakly-supervised-contrastive","title":"Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus Images","date":"2021-08-04","arxiv_id":"2108.02122","repositories_listed":1,"syntology":null},{"url":"/paper/approximating-attributed-incentive-salience","slug":"approximating-attributed-incentive-salience","title":"Approximating the Manifold Structure of Attributed Incentive Salience from Large Scale Behavioural Data. A Representation Learning Approach Based on Artificial Neural Networks","date":"2021-08-03","arxiv_id":"2108.01724","repositories_listed":1,"syntology":null},{"url":"/paper/improving-music-performance-assessment-with","slug":"improving-music-performance-assessment-with","title":"Improving Music Performance Assessment with Contrastive Learning","date":"2021-08-03","arxiv_id":"2108.01711","repositories_listed":1,"syntology":null},{"url":"/paper/congested-crowd-instance-localization-with","slug":"congested-crowd-instance-localization-with","title":"Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer","date":"2021-08-02","arxiv_id":"2108.00584","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-lottery-ticket-hypothesis-in-media","slug":"exploring-lottery-ticket-hypothesis-in-media","title":"Exploring Lottery Ticket Hypothesis in Media Recommender Systems","date":"2021-08-02","arxiv_id":"2108.00944","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-for-neural-population","slug":"representation-learning-for-neural-population","title":"Representation learning for neural population activity with Neural Data Transformers","date":"2021-08-02","arxiv_id":"2108.01210","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":4,"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, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/representation-learning-for-neural-population#ran","syntology_url":"https://syntology.ai/paper/2108.01210","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.01210"}},"official":{"repos":["snel-repo/neural-data-transformers"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-audiovisual-representation","slug":"self-supervised-audiovisual-representation","title":"Self-supervised Audiovisual Representation Learning for Remote Sensing Data","date":"2021-08-02","arxiv_id":"2108.00688","repositories_listed":1,"syntology":null},{"url":"/paper/a-structure-self-aware-model-for-discourse","slug":"a-structure-self-aware-model-for-discourse","title":"A Structure Self-Aware Model for Discourse Parsing on Multi-Party Dialogues","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-evaluation-of-disentangled-representation","slug":"an-evaluation-of-disentangled-representation","title":"An Evaluation of Disentangled Representation Learning for Texts","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/bootstrapped-unsupervised-sentence","slug":"bootstrapped-unsupervised-sentence","title":"Bootstrapped Unsupervised Sentence Representation Learning","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/decaf-deep-extreme-classification-with-label","slug":"decaf-deep-extreme-classification-with-label","title":"DECAF: Deep Extreme Classification with Label Features","date":"2021-08-01","arxiv_id":"2108.00368","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/decaf-deep-extreme-classification-with-label#ran","syntology_url":"https://syntology.ai/paper/2108.00368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00368"}},"official":{"repos":["Extreme-classification/DECAF"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/excar-event-graph-knowledge-enhanced","slug":"excar-event-graph-knowledge-enhanced","title":"ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learn-the-big-picture-representation-learning","slug":"learn-the-big-picture-representation-learning","title":"Learn The Big Picture: Representation Learning for Clustering","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semantic-relation-aware-difference","slug":"semantic-relation-aware-difference","title":"Semantic Relation-aware Difference Representation Learning for Change Captioning","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/eclare-extreme-classification-with-label","slug":"eclare-extreme-classification-with-label","title":"ECLARE: Extreme Classification with Label Graph Correlations","date":"2021-07-31","arxiv_id":"2108.00261","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/eclare-extreme-classification-with-label#ran","syntology_url":"https://syntology.ai/paper/2108.00261","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00261"}},"official":{"repos":["Extreme-classification/ECLARE"],"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/learning-instance-level-spatial-temporal","slug":"learning-instance-level-spatial-temporal","title":"Learning Instance-level Spatial-Temporal Patterns for Person Re-identification","date":"2021-07-31","arxiv_id":"2108.00171","repositories_listed":1,"syntology":null},{"url":"/paper/object-aware-contrastive-learning-for","slug":"object-aware-contrastive-learning-for","title":"Object-aware Contrastive Learning for Debiased Scene Representation","date":"2021-07-30","arxiv_id":"2108.00049","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-self-supervised-augmented","slug":"hierarchical-self-supervised-augmented","title":"Hierarchical Self-supervised Augmented Knowledge Distillation","date":"2021-07-29","arxiv_id":"2107.13715","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/hierarchical-self-supervised-augmented#ran","syntology_url":"https://syntology.ai/paper/2107.13715","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.13715"}},"official":{"repos":["winycg/HSAKD"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/learning-geometry-guided-depth-via-projective","slug":"learning-geometry-guided-depth-via-projective","title":"Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection","date":"2021-07-29","arxiv_id":"2107.13931","repositories_listed":1,"syntology":null},{"url":"/paper/checking-patch-behaviour-against-test","slug":"checking-patch-behaviour-against-test","title":"Predicting Patch Correctness Based on the Similarity of Failing Test Cases","date":"2021-07-28","arxiv_id":"2107.13296","repositories_listed":1,"syntology":null},{"url":"/paper/graph-constrained-data-representation","slug":"graph-constrained-data-representation","title":"Graph Constrained Data Representation Learning for Human Motion Segmentation","date":"2021-07-28","arxiv_id":"2107.13362","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/graph-constrained-data-representation#ran","syntology_url":"https://syntology.ai/paper/2107.13362","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.13362"}},"official":{"repos":["mdimiccoli/gcrl-for-hms"],"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/mwp-bert-a-strong-baseline-for-math-word","slug":"mwp-bert-a-strong-baseline-for-math-word","title":"MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving","date":"2021-07-28","arxiv_id":"2107.13435","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mwp-bert-a-strong-baseline-for-math-word#ran","syntology_url":"https://syntology.ai/paper/2107.13435","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.13435"}},"official":{"repos":["lzhenwen/mwp-bert"],"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","unlocated"]}}},{"url":"/paper/ccgl-contrastive-cascade-graph-learning","slug":"ccgl-contrastive-cascade-graph-learning","title":"CCGL: Contrastive Cascade Graph Learning","date":"2021-07-27","arxiv_id":"2107.12576","repositories_listed":1,"syntology":null},{"url":"/paper/triplet-is-all-you-need-with-random-mappings","slug":"triplet-is-all-you-need-with-random-mappings","title":"Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings","date":"2021-07-22","arxiv_id":"2107.10419","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-sound-generation-using-neural","slug":"conditional-sound-generation-using-neural","title":"Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning","date":"2021-07-21","arxiv_id":"2107.09998","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/conditional-sound-generation-using-neural#ran","syntology_url":"https://syntology.ai/paper/2107.09998","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.09998"}},"official":{"repos":["liuxubo717/sound_generation"],"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/discovering-latent-causal-variables-via","slug":"discovering-latent-causal-variables-via","title":"Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA","date":"2021-07-21","arxiv_id":"2107.10098","repositories_listed":1,"syntology":null},{"url":"/paper/bype-vae-bayesian-pseudocoresets-exemplar-vae","slug":"bype-vae-bayesian-pseudocoresets-exemplar-vae","title":"ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE","date":"2021-07-20","arxiv_id":"2107.09286","repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-graph-representation-learning","slug":"large-scale-graph-representation-learning","title":"Large-scale graph representation learning with very deep GNNs and self-supervision","date":"2021-07-20","arxiv_id":"2107.09422","repositories_listed":1,"syntology":null},{"url":"/paper/mimo-mutual-integration-of-patient-journey","slug":"mimo-mutual-integration-of-patient-journey","title":"MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning","date":"2021-07-20","arxiv_id":"2107.09288","repositories_listed":1,"syntology":null},{"url":"/paper/wikigraphs-a-wikipedia-text-knowledge-graph","slug":"wikigraphs-a-wikipedia-text-knowledge-graph","title":"WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset","date":"2021-07-20","arxiv_id":"2107.09556","repositories_listed":1,"syntology":null},{"url":"/paper/learning-attributed-graph-representations","slug":"learning-attributed-graph-representations","title":"Learning Attributed Graph Representations with Communicative Message Passing Transformer","date":"2021-07-19","arxiv_id":"2107.08773","repositories_listed":1,"syntology":null},{"url":"/paper/aspect-based-sentiment-analysis-using-bert","slug":"aspect-based-sentiment-analysis-using-bert","title":"Aspect-based Sentiment Analysis using BERT with Disentangled Attention","date":"2021-07-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/visual-representation-learning-does-not","slug":"visual-representation-learning-does-not","title":"Visual Representation Learning Does Not Generalize Strongly Within the Same Domain","date":"2021-07-17","arxiv_id":"2107.08221","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/visual-representation-learning-does-not#ran","syntology_url":"https://syntology.ai/paper/2107.08221","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.08221"}},"official":{"repos":["bethgelab/InDomainGeneralizationBenchmark"],"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/graph-representation-learning-for-road-type","slug":"graph-representation-learning-for-road-type","title":"Graph Representation Learning for Road Type Classification","date":"2021-07-16","arxiv_id":"2107.07791","repositories_listed":1,"syntology":null},{"url":"/paper/neural-contextual-anomaly-detection-for-time","slug":"neural-contextual-anomaly-detection-for-time","title":"Neural Contextual Anomaly Detection for Time Series","date":"2021-07-16","arxiv_id":"2107.07702","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-framework-for-remote","slug":"self-supervised-learning-framework-for-remote","title":"Self-supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss","date":"2021-07-16","arxiv_id":"2107.07695","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/self-supervised-learning-framework-for-remote#ran","syntology_url":"https://syntology.ai/paper/2107.07695","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.07695"}},"official":{"repos":["Dylan-H-Wang/SLF-RPM"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/temporal-aware-language-representation","slug":"temporal-aware-language-representation","title":"Temporal-aware Language Representation Learning From Crowdsourced Labels","date":"2021-07-15","arxiv_id":"2107.07958","repositories_listed":1,"syntology":null},{"url":"/paper/nucmm-dataset-3d-neuronal-nuclei-instance","slug":"nucmm-dataset-3d-neuronal-nuclei-instance","title":"NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale","date":"2021-07-13","arxiv_id":"2107.05840","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/nucmm-dataset-3d-neuronal-nuclei-instance#ran","syntology_url":"https://syntology.ai/paper/2107.05840","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.05840"}},"official":{"repos":["zudi-lin/pytorch_connectomics"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-designing-good-representation-learning","slug":"on-designing-good-representation-learning","title":"Clustering-Based Representation Learning through Output Translation and Its Application to Remote--Sensing Images","date":"2021-07-13","arxiv_id":"2107.05948","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-learning-for-cold-start","slug":"contrastive-learning-for-cold-start","title":"Contrastive Learning for Cold-Start Recommendation","date":"2021-07-12","arxiv_id":"2107.05315","repositories_listed":1,"syntology":null},{"url":"/paper/geographical-knowledge-driven-representation","slug":"geographical-knowledge-driven-representation","title":"Geographical Knowledge-driven Representation Learning for Remote Sensing Images","date":"2021-07-12","arxiv_id":"2107.05276","repositories_listed":1,"syntology":null},{"url":"/paper/moocrep-a-unified-pre-trained-embedding-of","slug":"moocrep-a-unified-pre-trained-embedding-of","title":"MOOCRep: A Unified Pre-trained Embedding of MOOC Entities","date":"2021-07-12","arxiv_id":"2107.05154","repositories_listed":1,"syntology":null},{"url":"/paper/discrete-time-temporal-network-embedding-via","slug":"discrete-time-temporal-network-embedding-via","title":"Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space","date":"2021-07-08","arxiv_id":"2107.03767","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-multimodal-fusion-with","slug":"contrastive-multimodal-fusion-with","title":"Contrastive Multimodal Fusion with TupleInfoNCE","date":"2021-07-06","arxiv_id":"2107.02575","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/contrastive-multimodal-fusion-with#ran","syntology_url":"https://syntology.ai/paper/2107.02575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.02575"}},"official":{"repos":["hoi4d/TupleInfoNCE"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/continual-contrastive-self-supervised","slug":"continual-contrastive-self-supervised","title":"Continual Contrastive Learning for Image Classification","date":"2021-07-05","arxiv_id":"2107.01776","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/continual-contrastive-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2107.01776","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.01776"}},"official":{"repos":["VDIGPKU/ContinualContrastiveLearning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dppin-a-biological-dataset-of-dynamic-protein","slug":"dppin-a-biological-dataset-of-dynamic-protein","title":"DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data","date":"2021-07-05","arxiv_id":"2107.02168","repositories_listed":1,"syntology":null},{"url":"/paper/improved-representation-learning-for-session","slug":"improved-representation-learning-for-session","title":"Introducing Self-Attention to Target Attentive Graph Neural Networks","date":"2021-07-04","arxiv_id":"2107.01516","repositories_listed":1,"syntology":null},{"url":"/paper/how-incomplete-is-contrastive-learning","slug":"how-incomplete-is-contrastive-learning","title":"Inter-intra Variant Dual Representations forSelf-supervised Video Recognition","date":"2021-07-02","arxiv_id":"2107.01194","repositories_listed":1,"syntology":null},{"url":"/paper/systematic-evaluation-of-causal-discovery-in-1","slug":"systematic-evaluation-of-causal-discovery-in-1","title":"Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning","date":"2021-07-02","arxiv_id":"2107.00848","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 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; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/systematic-evaluation-of-causal-discovery-in-1#ran","syntology_url":"https://syntology.ai/paper/2107.00848","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.00848"}},"official":{"repos":["dido1998/CausalMBRL"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/generalization-and-robustness-implications-in","slug":"generalization-and-robustness-implications-in","title":"Generalization and Robustness Implications in Object-Centric Learning","date":"2021-07-01","arxiv_id":"2107.00637","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generalization-and-robustness-implications-in#ran","syntology_url":"https://syntology.ai/paper/2107.00637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.00637"}},"official":{"repos":["addtt/object-centric-library"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pretext-tasks-selection-for-multitask-self","slug":"pretext-tasks-selection-for-multitask-self","title":"Pretext Tasks selection for multitask self-supervised speech representation learning","date":"2021-07-01","arxiv_id":"2107.00594","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":2,"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/pretext-tasks-selection-for-multitask-self#ran","syntology_url":"https://syntology.ai/paper/2107.00594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.00594"}},"official":{"repos":["salah-zaiem/PL-groupselection"],"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/interventional-assays-for-the-latent-space-of","slug":"interventional-assays-for-the-latent-space-of","title":"Exploring the Latent Space of Autoencoders with Interventional Assays","date":"2021-06-30","arxiv_id":"2106.16091","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/interventional-assays-for-the-latent-space-of#ran","syntology_url":"https://syntology.ai/paper/2106.16091","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.16091"}},"official":{"repos":["felixludos/latent-responses"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-representation-learning-perspective-on-the","slug":"a-representation-learning-perspective-on-the","title":"A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning","date":"2021-06-29","arxiv_id":"2106.15615","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/a-representation-learning-perspective-on-the#ran","syntology_url":"https://syntology.ai/paper/2106.15615","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.15615"}},"official":{"repos":["nsaunshi/meta_tr_val_split"],"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/geometry-aware-transformer-for-molecular","slug":"geometry-aware-transformer-for-molecular","title":"GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning","date":"2021-06-29","arxiv_id":"2106.15516","repositories_listed":1,"syntology":null},{"url":"/paper/opencos-contrastive-semi-supervised-learning-1","slug":"opencos-contrastive-semi-supervised-learning-1","title":"OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data","date":"2021-06-29","arxiv_id":"2107.08943","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":2,"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/opencos-contrastive-semi-supervised-learning-1#ran","syntology_url":"https://syntology.ai/paper/2107.08943","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.08943"}},"official":{"repos":["alinlab/opencos"],"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/scarf-self-supervised-contrastive-learning","slug":"scarf-self-supervised-contrastive-learning","title":"SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption","date":"2021-06-29","arxiv_id":"2106.15147","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"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 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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/scarf-self-supervised-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2106.15147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.15147"}},"official":null}},{"url":"/paper/hyperbolic-busemann-learning-with-ideal","slug":"hyperbolic-busemann-learning-with-ideal","title":"Hyperbolic Busemann Learning with Ideal Prototypes","date":"2021-06-28","arxiv_id":"2106.14472","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/hyperbolic-busemann-learning-with-ideal#ran","syntology_url":"https://syntology.ai/paper/2106.14472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.14472"}},"official":{"repos":["minaghadimiatigh/hyperbolic-busemann-learning"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/litegem-lite-geometry-enhanced-molecular","slug":"litegem-lite-geometry-enhanced-molecular","title":"LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction","date":"2021-06-28","arxiv_id":"2106.14494","repositories_listed":1,"syntology":null},{"url":"/paper/word2box-learning-word-representation-using","slug":"word2box-learning-word-representation-using","title":"Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings","date":"2021-06-28","arxiv_id":"2106.14361","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-network-representation-learning","slug":"interpretable-network-representation-learning","title":"Interpretable Network Representation Learning with Principal Component Analysis","date":"2021-06-27","arxiv_id":"2106.14238","repositories_listed":1,"syntology":null},{"url":"/paper/projection-wise-disentangling-for-fair-and","slug":"projection-wise-disentangling-for-fair-and","title":"Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis","date":"2021-06-25","arxiv_id":"2106.13734","repositories_listed":1,"syntology":null},{"url":"/paper/from-canonical-correlation-analysis-to-self","slug":"from-canonical-correlation-analysis-to-self","title":"From Canonical Correlation Analysis to Self-supervised Graph Neural Networks","date":"2021-06-23","arxiv_id":"2106.12484","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":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/from-canonical-correlation-analysis-to-self#ran","syntology_url":"https://syntology.ai/paper/2106.12484","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.12484"}},"official":{"repos":["hengruizhang98/CCA-SSG"],"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/unsupervised-speech-enhancement-using","slug":"unsupervised-speech-enhancement-using","title":"Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders","date":"2021-06-23","arxiv_id":"2106.12271","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-latent-space-model-for-graph","slug":"a-deep-latent-space-model-for-graph","title":"A Deep Latent Space Model for Graph Representation Learning","date":"2021-06-22","arxiv_id":"2106.11721","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-representational-power-of-graph","slug":"exploring-the-representational-power-of-graph","title":"Exploring the Representational Power of Graph Autoencoder","date":"2021-06-22","arxiv_id":"2106.12005","repositories_listed":1,"syntology":null},{"url":"/paper/finding-valid-adjustments-under-non","slug":"finding-valid-adjustments-under-non","title":"Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge","date":"2021-06-22","arxiv_id":"2106.11560","repositories_listed":1,"syntology":null},{"url":"/paper/give-me-your-trained-model-domain-adaptive","slug":"give-me-your-trained-model-domain-adaptive","title":"A Curriculum-style Self-training Approach for Source-Free Semantic Segmentation","date":"2021-06-22","arxiv_id":"2106.11653","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/give-me-your-trained-model-domain-adaptive#ran","syntology_url":"https://syntology.ai/paper/2106.11653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11653"}},"official":{"repos":["yxiwang/atp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/manifold-alignment-across-geometric-spaces","slug":"manifold-alignment-across-geometric-spaces","title":"Manifold Alignment across Geometric Spaces for Knowledge Base Representation Learning","date":"2021-06-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-object-level-representation","slug":"unsupervised-object-level-representation","title":"Unsupervised Object-Level Representation Learning from Scene Images","date":"2021-06-22","arxiv_id":"2106.11952","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":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) · 0 unverified","sample_list":"/paper/unsupervised-object-level-representation#ran","syntology_url":"https://syntology.ai/paper/2106.11952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11952"}},"official":{"repos":["jiahao000/orl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/3d-object-detection-for-autonomous-driving-a","slug":"3d-object-detection-for-autonomous-driving-a","title":"3D Object Detection for Autonomous Driving: A Survey","date":"2021-06-21","arxiv_id":"2106.10823","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":3,"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/3d-object-detection-for-autonomous-driving-a#ran","syntology_url":"https://syntology.ai/paper/2106.10823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10823"}},"official":{"repos":["rui-qian/SoTA-3D-Object-Detection"],"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/convdysat-deep-neural-representation-learning","slug":"convdysat-deep-neural-representation-learning","title":"ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural Networks","date":"2021-06-21","arxiv_id":"2106.11430","repositories_listed":1,"syntology":null},{"url":"/paper/digs-divergence-guided-shape-implicit-neural","slug":"digs-divergence-guided-shape-implicit-neural","title":"DiGS : Divergence guided shape implicit neural representation for unoriented point clouds","date":"2021-06-21","arxiv_id":"2106.10811","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/digs-divergence-guided-shape-implicit-neural#ran","syntology_url":"https://syntology.ai/paper/2106.10811","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10811"}},"official":{"repos":["Chumbyte/DiGS"],"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/visual-probing-cognitive-framework-for","slug":"visual-probing-cognitive-framework-for","title":"Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations","date":"2021-06-21","arxiv_id":"2106.11054","repositories_listed":1,"syntology":null},{"url":"/paper/jointgt-graph-text-joint-representation","slug":"jointgt-graph-text-joint-representation","title":"JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs","date":"2021-06-19","arxiv_id":"2106.10502","repositories_listed":1,"syntology":null},{"url":"/paper/learning-the-predictability-of-the-future","slug":"learning-the-predictability-of-the-future","title":"Learning the Predictability of the Future","date":"2021-06-19","arxiv_id":"2101.01600","repositories_listed":1,"syntology":{"n":4,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 4 unverified","sample_list":"/paper/learning-the-predictability-of-the-future#ran","syntology_url":"https://syntology.ai/paper/2101.01600","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.01600"}},"official":{"repos":["cvlab-columbia/hyperfuture"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":[]}}},{"url":"/paper/probabilistic-model-distillation-for-semantic","slug":"probabilistic-model-distillation-for-semantic","title":"Probabilistic Model Distillation for Semantic Correspondence","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/selfsagcn-self-supervised-semantic-alignment","slug":"selfsagcn-self-supervised-semantic-alignment","title":"SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"a736837754d2df9da9a9f2bbb6cf36bfd797ea8e3d6be652e6769eb99b3d4847","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}