{"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/27","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":27,"pages_in_order":106,"rows_per_page":100,"rows":[2601,2700],"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/26","next":"/task/representation-learning/papers/28","papers":[{"url":"/paper/distilling-representations-from-gan-generator","slug":"distilling-representations-from-gan-generator","title":"Distilling Representations from GAN Generator via Squeeze and Span","date":"2022-11-06","arxiv_id":"2211.03000","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/distilling-representations-from-gan-generator#ran","syntology_url":"https://syntology.ai/paper/2211.03000","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.03000"}},"official":{"repos":["yangyu12/squeeze-and-span"],"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/adversarial-causal-augmentation-for-graph","slug":"adversarial-causal-augmentation-for-graph","title":"Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift","date":"2022-11-05","arxiv_id":"2211.02843","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/adversarial-causal-augmentation-for-graph#ran","syntology_url":"https://syntology.ai/paper/2211.02843","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.02843"}},"official":{"repos":["yongduosui/aia"],"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/small-language-models-for-tabular-data","slug":"small-language-models-for-tabular-data","title":"Small Language Models for Tabular Data","date":"2022-11-05","arxiv_id":"2211.02941","repositories_listed":1,"syntology":null},{"url":"/paper/geometry-complete-perceptron-networks-for-3d","slug":"geometry-complete-perceptron-networks-for-3d","title":"Geometry-Complete Perceptron Networks for 3D Molecular Graphs","date":"2022-11-04","arxiv_id":"2211.02504","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-visual-representation-learning-5","slug":"unsupervised-visual-representation-learning-5","title":"Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment","date":"2022-11-04","arxiv_id":"2211.02284","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/unsupervised-visual-representation-learning-5#ran","syntology_url":"https://syntology.ai/paper/2211.02284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.02284"}},"official":{"repos":["movinghoon/mira"],"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/adversarial-auto-augment-with-label","slug":"adversarial-auto-augment-with-label","title":"Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach","date":"2022-11-02","arxiv_id":"2211.00824","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/adversarial-auto-augment-with-label#ran","syntology_url":"https://syntology.ai/paper/2211.00824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.00824"}},"official":{"repos":["kai-wen-yang/lpa3"],"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/behavior-prior-representation-learning-for","slug":"behavior-prior-representation-learning-for","title":"Behavior Prior Representation learning for Offline Reinforcement Learning","date":"2022-11-02","arxiv_id":"2211.00863","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/behavior-prior-representation-learning-for#ran","syntology_url":"https://syntology.ai/paper/2211.00863","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.00863"}},"official":{"repos":["bit1029public/offline_bpr"],"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/chinese-clip-contrastive-vision-language","slug":"chinese-clip-contrastive-vision-language","title":"Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese","date":"2022-11-02","arxiv_id":"2211.01335","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chinese-clip-contrastive-vision-language#ran","syntology_url":"https://syntology.ai/paper/2211.01335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.01335"}},"official":{"repos":["ofa-sys/chinese-clip"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/data2vec-aqc-search-for-the-right-teaching","slug":"data2vec-aqc-search-for-the-right-teaching","title":"data2vec-aqc: Search for the right Teaching Assistant in the Teacher-Student training setup","date":"2022-11-02","arxiv_id":"2211.01246","repositories_listed":1,"syntology":null},{"url":"/paper/deep-multimodal-fusion-for-generalizable","slug":"deep-multimodal-fusion-for-generalizable","title":"Deep Multimodal Fusion for Generalizable Person Re-identification","date":"2022-11-02","arxiv_id":"2211.00933","repositories_listed":1,"syntology":null},{"url":"/paper/slicer-learning-universal-audio","slug":"slicer-learning-universal-audio","title":"SLICER: Learning universal audio representations using low-resource self-supervised pre-training","date":"2022-11-02","arxiv_id":"2211.01519","repositories_listed":1,"syntology":null},{"url":"/paper/improving-variational-autoencoders-with","slug":"improving-variational-autoencoders-with","title":"Improving Variational Autoencoders with Density Gap-based Regularization","date":"2022-11-01","arxiv_id":"2211.00321","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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) · 1 unverified","sample_list":"/paper/improving-variational-autoencoders-with#ran","syntology_url":"https://syntology.ai/paper/2211.00321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.00321"}},"official":{"repos":["zhangjf-nlp/dg-vaes"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/position-aware-subgraph-neural-networks-with","slug":"position-aware-subgraph-neural-networks-with","title":"Position-Aware Subgraph Neural Networks with Data-Efficient Learning","date":"2022-11-01","arxiv_id":"2211.00572","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-character-to-character","slug":"self-supervised-character-to-character","title":"Self-supervised Character-to-Character Distillation for Text Recognition","date":"2022-11-01","arxiv_id":"2211.00288","repositories_listed":1,"syntology":null},{"url":"/paper/a-robust-estimator-of-mutual-information-for","slug":"a-robust-estimator-of-mutual-information-for","title":"A robust estimator of mutual information for deep learning interpretability","date":"2022-10-31","arxiv_id":"2211.00024","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-un-controllable-features","slug":"disentangled-un-controllable-features","title":"Disentangled (Un)Controllable Features","date":"2022-10-31","arxiv_id":"2211.00086","repositories_listed":1,"syntology":null},{"url":"/paper/lipschitz-regularized-gradient-flows-and","slug":"lipschitz-regularized-gradient-flows-and","title":"Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data","date":"2022-10-31","arxiv_id":"2210.17230","repositories_listed":1,"syntology":null},{"url":"/paper/page-prototype-based-model-level-explanations","slug":"page-prototype-based-model-level-explanations","title":"PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks","date":"2022-10-31","arxiv_id":"2210.17159","repositories_listed":1,"syntology":null},{"url":"/paper/the-numerical-stability-of-hyperbolic","slug":"the-numerical-stability-of-hyperbolic","title":"The Numerical Stability of Hyperbolic Representation Learning","date":"2022-10-31","arxiv_id":"2211.00181","repositories_listed":1,"syntology":null},{"url":"/paper/towards-relation-centered-pooling-and","slug":"towards-relation-centered-pooling-and","title":"Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks","date":"2022-10-31","arxiv_id":"2210.17142","repositories_listed":1,"syntology":null},{"url":"/paper/unified-optimal-transport-framework-for","slug":"unified-optimal-transport-framework-for","title":"Unified Optimal Transport Framework for Universal Domain Adaptation","date":"2022-10-31","arxiv_id":"2210.17067","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unified-optimal-transport-framework-for#ran","syntology_url":"https://syntology.ai/paper/2210.17067","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.17067"}},"official":{"repos":["changwxx/uniot-for-unida"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/forget-embedding-layers-representation","slug":"forget-embedding-layers-representation","title":"FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems","date":"2022-10-30","arxiv_id":"2210.16928","repositories_listed":1,"syntology":null},{"url":"/paper/generate-discriminate-and-contrast-a-semi","slug":"generate-discriminate-and-contrast-a-semi","title":"Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework","date":"2022-10-30","arxiv_id":"2210.16798","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/generate-discriminate-and-contrast-a-semi#ran","syntology_url":"https://syntology.ai/paper/2210.16798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16798"}},"official":{"repos":["matthewcym/gense"],"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":["found_in_text"]}}},{"url":"/paper/differentiable-data-augmentation-for","slug":"differentiable-data-augmentation-for","title":"Differentiable Data Augmentation for Contrastive Sentence Representation Learning","date":"2022-10-29","arxiv_id":"2210.16536","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/differentiable-data-augmentation-for#ran","syntology_url":"https://syntology.ai/paper/2210.16536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16536"}},"official":{"repos":["tianduowang/diffaug"],"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/rare-wildlife-recognition-with-self","slug":"rare-wildlife-recognition-with-self","title":"Rare Wildlife Recognition with Self-Supervised Representation Learning","date":"2022-10-29","arxiv_id":"2211.05636","repositories_listed":1,"syntology":null},{"url":"/paper/speaker-representation-learning-via","slug":"speaker-representation-learning-via","title":"Speaker Representation Learning via Contrastive Loss with Maximal Speaker Separability","date":"2022-10-29","arxiv_id":"2210.16636","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/speaker-representation-learning-via#ran","syntology_url":"https://syntology.ai/paper/2210.16636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16636"}},"official":{"repos":["shanmon110/aamsupcon"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-survey-on-causal-representation-learning","slug":"a-survey-on-causal-representation-learning","title":"A Survey on Causal Representation Learning and Future Work for Medical Image Analysis","date":"2022-10-28","arxiv_id":"2210.16034","repositories_listed":1,"syntology":null},{"url":"/paper/domain-generalization-through-the-lens-of","slug":"domain-generalization-through-the-lens-of","title":"Domain Generalization through the Lens of Angular Invariance","date":"2022-10-28","arxiv_id":"2210.15836","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"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 1 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/domain-generalization-through-the-lens-of#ran","syntology_url":"https://syntology.ai/paper/2210.15836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.15836"}},"official":{"repos":["jinyujie99/aidgn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/gm-tcnet-gated-multi-scale-temporal","slug":"gm-tcnet-gated-multi-scale-temporal","title":"GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition","date":"2022-10-28","arxiv_id":"2210.15834","repositories_listed":1,"syntology":null},{"url":"/paper/resus-warm-up-cold-users-via-meta-learning","slug":"resus-warm-up-cold-users-via-meta-learning","title":"RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction","date":"2022-10-28","arxiv_id":"2210.16080","repositories_listed":1,"syntology":null},{"url":"/paper/speaker-recognition-with-two-step-multi-modal","slug":"speaker-recognition-with-two-step-multi-modal","title":"Speaker recognition with two-step multi-modal deep cleansing","date":"2022-10-28","arxiv_id":"2210.15903","repositories_listed":1,"syntology":null},{"url":"/paper/gaitmixer-skeleton-based-gait-representation","slug":"gaitmixer-skeleton-based-gait-representation","title":"GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer","date":"2022-10-27","arxiv_id":"2210.15491","repositories_listed":1,"syntology":null},{"url":"/paper/multi-dimensional-edge-based-audio-event","slug":"multi-dimensional-edge-based-audio-event","title":"Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene Classification","date":"2022-10-27","arxiv_id":"2210.15366","repositories_listed":1,"syntology":null},{"url":"/paper/robust-data2vec-noise-robust-speech","slug":"robust-data2vec-noise-robust-speech","title":"Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive Learning","date":"2022-10-27","arxiv_id":"2210.15324","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-contrastive-learning-for-3","slug":"supervised-contrastive-learning-for-3","title":"Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning","date":"2022-10-27","arxiv_id":"2210.16192","repositories_listed":1,"syntology":null},{"url":"/paper/fedclassavg-local-representation-learning-for","slug":"fedclassavg-local-representation-learning-for","title":"FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks","date":"2022-10-25","arxiv_id":"2210.14226","repositories_listed":1,"syntology":null},{"url":"/paper/mew-unet-multi-axis-representation-learning","slug":"mew-unet-multi-axis-representation-learning","title":"MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentation","date":"2022-10-25","arxiv_id":"2210.14007","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-representation-learning-for-gaze","slug":"contrastive-representation-learning-for-gaze","title":"Contrastive Representation Learning for Gaze Estimation","date":"2022-10-24","arxiv_id":"2210.13404","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":3,"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/contrastive-representation-learning-for-gaze#ran","syntology_url":"https://syntology.ai/paper/2210.13404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13404"}},"official":{"repos":["jswati31/gazeclr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/transformers-over-directed-acyclic-graphs-1","slug":"transformers-over-directed-acyclic-graphs-1","title":"Transformers over Directed Acyclic Graphs","date":"2022-10-24","arxiv_id":"2210.13148","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/transformers-over-directed-acyclic-graphs-1#ran","syntology_url":"https://syntology.ai/paper/2210.13148","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13148"}},"official":null}},{"url":"/paper/unsupervised-object-representation-learning","slug":"unsupervised-object-representation-learning","title":"Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE","date":"2022-10-24","arxiv_id":"2210.12918","repositories_listed":1,"syntology":{"n":20,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/unsupervised-object-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2210.12918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12918"}},"official":{"repos":["smlc-nysbc/target-vae"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-eigenfunctions-are-structured","slug":"neural-eigenfunctions-are-structured","title":"Neural Eigenfunctions Are Structured Representation Learners","date":"2022-10-23","arxiv_id":"2210.12637","repositories_listed":1,"syntology":null},{"url":"/paper/graph-coloring-via-neural-networks-for","slug":"graph-coloring-via-neural-networks-for","title":"Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction","date":"2022-10-21","arxiv_id":"2210.12158","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/graph-coloring-via-neural-networks-for#ran","syntology_url":"https://syntology.ai/paper/2210.12158","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12158"}},"official":{"repos":["xuehansheng/neurhap"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/does-decentralized-learning-with-non-iid","slug":"does-decentralized-learning-with-non-iid","title":"Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?","date":"2022-10-20","arxiv_id":"2210.10947","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":0,"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/does-decentralized-learning-with-non-iid#ran","syntology_url":"https://syntology.ai/paper/2210.10947","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.10947"}},"official":{"repos":["liruiw/dec-ssl"],"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/freeze-then-train-towards-provable","slug":"freeze-then-train-towards-provable","title":"Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise","date":"2022-10-20","arxiv_id":"2210.11075","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/freeze-then-train-towards-provable#ran","syntology_url":"https://syntology.ai/paper/2210.11075","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11075"}},"official":{"repos":["ywolfeee/freeze-then-train"],"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/multitasking-models-are-robust-to-structural","slug":"multitasking-models-are-robust-to-structural","title":"Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve","date":"2022-10-20","arxiv_id":"2210.11618","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multitasking-models-are-robust-to-structural#ran","syntology_url":"https://syntology.ai/paper/2210.11618","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11618"}},"official":{"repos":["giannisdaras/multilingual_robustness"],"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/representation-learning-with-diffusion-models","slug":"representation-learning-with-diffusion-models","title":"Representation Learning with Diffusion Models","date":"2022-10-20","arxiv_id":"2210.11058","repositories_listed":1,"syntology":null},{"url":"/paper/vibus-data-efficient-3d-scene-parsing-with","slug":"vibus-data-efficient-3d-scene-parsing-with","title":"VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modeling","date":"2022-10-20","arxiv_id":"2210.11472","repositories_listed":1,"syntology":null},{"url":"/paper/anomaly-detection-requires-better","slug":"anomaly-detection-requires-better","title":"Anomaly Detection Requires Better Representations","date":"2022-10-19","arxiv_id":"2210.10773","repositories_listed":1,"syntology":null},{"url":"/paper/clutr-curriculum-learning-via-unsupervised","slug":"clutr-curriculum-learning-via-unsupervised","title":"CLUTR: Curriculum Learning via Unsupervised Task Representation Learning","date":"2022-10-19","arxiv_id":"2210.10243","repositories_listed":1,"syntology":null},{"url":"/paper/croco-self-supervised-pre-training-for-3d","slug":"croco-self-supervised-pre-training-for-3d","title":"CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion","date":"2022-10-19","arxiv_id":"2210.10716","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":7,"n_ran_checked":8,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"12 ran (of which 7 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/croco-self-supervised-pre-training-for-3d#ran","syntology_url":"https://syntology.ai/paper/2210.10716","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.10716"}},"official":{"repos":["naver/croco"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":7,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-modal-fusion-distillation-for-fine","slug":"cross-modal-fusion-distillation-for-fine","title":"Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval","date":"2022-10-19","arxiv_id":"2210.10486","repositories_listed":1,"syntology":null},{"url":"/paper/dyted-disentangling-temporal-invariance-and","slug":"dyted-disentangling-temporal-invariance-and","title":"DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph","date":"2022-10-19","arxiv_id":"2210.10592","repositories_listed":1,"syntology":null},{"url":"/paper/graphcspn-geometry-aware-depth-completion-via","slug":"graphcspn-geometry-aware-depth-completion-via","title":"GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs","date":"2022-10-19","arxiv_id":"2210.10758","repositories_listed":1,"syntology":null},{"url":"/paper/improving-chinese-story-generation-via","slug":"improving-chinese-story-generation-via","title":"Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics","date":"2022-10-19","arxiv_id":"2210.10618","repositories_listed":1,"syntology":null},{"url":"/paper/multi-granularity-cross-modality","slug":"multi-granularity-cross-modality","title":"Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media","date":"2022-10-19","arxiv_id":"2210.14163","repositories_listed":1,"syntology":null},{"url":"/paper/schema-aware-reference-as-prompt-improves","slug":"schema-aware-reference-as-prompt-improves","title":"Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction","date":"2022-10-19","arxiv_id":"2210.10709","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"10 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/schema-aware-reference-as-prompt-improves#ran","syntology_url":"https://syntology.ai/paper/2210.10709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.10709"}},"official":{"repos":["zjunlp/RAP"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/type-supervised-sequence-labeling-based-on","slug":"type-supervised-sequence-labeling-based-on","title":"Type-supervised sequence labeling based on the heterogeneous star graph for named entity recognition","date":"2022-10-19","arxiv_id":"2210.10240","repositories_listed":1,"syntology":null},{"url":"/paper/uninl-aligning-representation-learning-with","slug":"uninl-aligning-representation-learning-with","title":"UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning","date":"2022-10-19","arxiv_id":"2210.10722","repositories_listed":1,"syntology":null},{"url":"/paper/vtc-improving-video-text-retrieval-with-user","slug":"vtc-improving-video-text-retrieval-with-user","title":"VTC: Improving Video-Text Retrieval with User Comments","date":"2022-10-19","arxiv_id":"2210.10820","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/vtc-improving-video-text-retrieval-with-user#ran","syntology_url":"https://syntology.ai/paper/2210.10820","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.10820"}},"official":null}},{"url":"/paper/deep-multi-representation-model-for-click","slug":"deep-multi-representation-model-for-click","title":"Deep Multi-Representation Model for Click-Through Rate Prediction","date":"2022-10-18","arxiv_id":"2210.10664","repositories_listed":1,"syntology":null},{"url":"/paper/depth-contrast-self-supervised-pretraining-on","slug":"depth-contrast-self-supervised-pretraining-on","title":"Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification","date":"2022-10-18","arxiv_id":"2210.10633","repositories_listed":1,"syntology":null},{"url":"/paper/generalizing-in-the-real-world-with","slug":"generalizing-in-the-real-world-with","title":"Generalizing in the Real World with Representation Learning","date":"2022-10-18","arxiv_id":"2210.09925","repositories_listed":1,"syntology":null},{"url":"/paper/towards-efficient-and-effective-self","slug":"towards-efficient-and-effective-self","title":"Towards Efficient and Effective Self-Supervised Learning of Visual Representations","date":"2022-10-18","arxiv_id":"2210.09866","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-efficient-and-effective-self#ran","syntology_url":"https://syntology.ai/paper/2210.09866","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09866"}},"official":{"repos":["val-iisc/effssl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mdgcf-multi-dependency-graph-collaborative","slug":"mdgcf-multi-dependency-graph-collaborative","title":"MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies","date":"2022-10-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mose-modality-split-and-ensemble-for","slug":"mose-modality-split-and-ensemble-for","title":"MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion","date":"2022-10-17","arxiv_id":"2210.08821","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/mose-modality-split-and-ensemble-for#ran","syntology_url":"https://syntology.ai/paper/2210.08821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.08821"}},"official":{"repos":["oreozhao/mose4mkgc"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/non-contrastive-learning-meets-language-image","slug":"non-contrastive-learning-meets-language-image","title":"Non-Contrastive Learning Meets Language-Image Pre-Training","date":"2022-10-17","arxiv_id":"2210.09304","repositories_listed":1,"syntology":null},{"url":"/paper/unifying-graph-contrastive-learning-with","slug":"unifying-graph-contrastive-learning-with","title":"Unifying Graph Contrastive Learning with Flexible Contextual Scopes","date":"2022-10-17","arxiv_id":"2210.08792","repositories_listed":1,"syntology":null},{"url":"/paper/watch-the-neighbors-a-unified-k-nearest","slug":"watch-the-neighbors-a-unified-k-nearest","title":"Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery","date":"2022-10-17","arxiv_id":"2210.08909","repositories_listed":1,"syntology":null},{"url":"/paper/sentence-representation-learning-with","slug":"sentence-representation-learning-with","title":"Sentence Representation Learning with Generative Objective rather than Contrastive Objective","date":"2022-10-16","arxiv_id":"2210.08474","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/sentence-representation-learning-with#ran","syntology_url":"https://syntology.ai/paper/2210.08474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.08474"}},"official":{"repos":["chengzhipanpan/paser"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/par-political-actor-representation-learning","slug":"par-political-actor-representation-learning","title":"PAR: Political Actor Representation Learning with Social Context and Expert Knowledge","date":"2022-10-15","arxiv_id":"2210.08362","repositories_listed":1,"syntology":null},{"url":"/paper/mico-a-multi-alternative-contrastive-learning","slug":"mico-a-multi-alternative-contrastive-learning","title":"MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation","date":"2022-10-14","arxiv_id":"2210.07570","repositories_listed":1,"syntology":null},{"url":"/paper/disentanglement-of-correlated-factors-via","slug":"disentanglement-of-correlated-factors-via","title":"Disentanglement of Correlated Factors via Hausdorff Factorized Support","date":"2022-10-13","arxiv_id":"2210.07347","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/disentanglement-of-correlated-factors-via#ran","syntology_url":"https://syntology.ai/paper/2210.07347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07347"}},"official":{"repos":["facebookresearch/disentangling-correlated-factors"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fare-provably-fair-representation-learning","slug":"fare-provably-fair-representation-learning","title":"FARE: Provably Fair Representation Learning with Practical Certificates","date":"2022-10-13","arxiv_id":"2210.07213","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/fare-provably-fair-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2210.07213","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07213"}},"official":{"repos":["eth-sri/fare"],"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/the-hidden-uniform-cluster-prior-in-self","slug":"the-hidden-uniform-cluster-prior-in-self","title":"The Hidden Uniform Cluster Prior in Self-Supervised Learning","date":"2022-10-13","arxiv_id":"2210.07277","repositories_listed":1,"syntology":null},{"url":"/paper/visual-reinforcement-learning-with-self","slug":"visual-reinforcement-learning-with-self","title":"Visual Reinforcement Learning with Self-Supervised 3D Representations","date":"2022-10-13","arxiv_id":"2210.07241","repositories_listed":1,"syntology":{"n":17,"n_ran":16,"n_constructed":0,"n_ran_checked":14,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":2,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/visual-reinforcement-learning-with-self#ran","syntology_url":"https://syntology.ai/paper/2210.07241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07241"}},"official":{"repos":["YanjieZe/rl3d"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-lower-bound-of-hash-codes-performance","slug":"a-lower-bound-of-hash-codes-performance","title":"A Lower Bound of Hash Codes' Performance","date":"2022-10-12","arxiv_id":"2210.05899","repositories_listed":1,"syntology":null},{"url":"/paper/entity-aware-negative-sampling-with-auxiliary","slug":"entity-aware-negative-sampling-with-auxiliary","title":"Entity Aware Negative Sampling with Auxiliary Loss of False Negative Prediction for Knowledge Graph Embedding","date":"2022-10-12","arxiv_id":"2210.06242","repositories_listed":1,"syntology":null},{"url":"/paper/language-agnostic-multilingual-information","slug":"language-agnostic-multilingual-information","title":"Language Agnostic Multilingual Information Retrieval with Contrastive Learning","date":"2022-10-12","arxiv_id":"2210.06633","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-contrastive-learning-for-evidence","slug":"adversarial-contrastive-learning-for-evidence","title":"Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks","date":"2022-10-11","arxiv_id":"2210.05498","repositories_listed":1,"syntology":null},{"url":"/paper/digat-modeling-news-recommendation-with-dual","slug":"digat-modeling-news-recommendation-with-dual","title":"DIGAT: Modeling News Recommendation with Dual-Graph Interaction","date":"2022-10-11","arxiv_id":"2210.05196","repositories_listed":1,"syntology":null},{"url":"/paper/mixed-modality-representation-learning-and","slug":"mixed-modality-representation-learning-and","title":"Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA","date":"2022-10-11","arxiv_id":"2210.05197","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mixed-modality-representation-learning-and#ran","syntology_url":"https://syntology.ai/paper/2210.05197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05197"}},"official":{"repos":["jun-jie-huang/otter"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/opera-omni-supervised-representation-learning","slug":"opera-omni-supervised-representation-learning","title":"OPERA: Omni-Supervised Representation Learning with Hierarchical Supervisions","date":"2022-10-11","arxiv_id":"2210.05557","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/opera-omni-supervised-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2210.05557","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05557"}},"official":{"repos":["wangck20/opera"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/pre-training-for-robots-offline-rl-enables","slug":"pre-training-for-robots-offline-rl-enables","title":"Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials","date":"2022-10-11","arxiv_id":"2210.05178","repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-baseline-that-questions-the-use-of","slug":"a-simple-baseline-that-questions-the-use-of","title":"A Simple Baseline that Questions the Use of Pretrained-Models in Continual Learning","date":"2022-10-10","arxiv_id":"2210.04428","repositories_listed":1,"syntology":null},{"url":"/paper/robust-diversified-graph-contrastive-network","slug":"robust-diversified-graph-contrastive-network","title":"Robust Diversified Graph Contrastive Network for Incomplete Multi-view Clustering","date":"2022-10-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/topicvae-topic-aware-disentanglement","slug":"topicvae-topic-aware-disentanglement","title":"TopicVAE: Topic-aware Disentanglement Representation Learning for Enhanced Recommendation","date":"2022-10-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-representation-learning-for-1","slug":"contrastive-representation-learning-for-1","title":"Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs","date":"2022-10-09","arxiv_id":"2210.04373","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-video-representation-learning-12","slug":"self-supervised-video-representation-learning-12","title":"Self-supervised Video Representation Learning with Motion-Aware Masked Autoencoders","date":"2022-10-09","arxiv_id":"2210.04154","repositories_listed":1,"syntology":null},{"url":"/paper/robustness-of-unsupervised-representation","slug":"robustness-of-unsupervised-representation","title":"Robustness of Unsupervised Representation Learning without Labels","date":"2022-10-08","arxiv_id":"2210.04076","repositories_listed":1,"syntology":null},{"url":"/paper/sda-simple-discrete-augmentation-for","slug":"sda-simple-discrete-augmentation-for","title":"SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning","date":"2022-10-08","arxiv_id":"2210.03963","repositories_listed":1,"syntology":null},{"url":"/paper/towards-real-time-temporal-graph-learning","slug":"towards-real-time-temporal-graph-learning","title":"Towards Real-Time Temporal Graph Learning","date":"2022-10-08","arxiv_id":"2210.04114","repositories_listed":1,"syntology":null},{"url":"/paper/augmentations-in-hypergraph-contrastive","slug":"augmentations-in-hypergraph-contrastive","title":"Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative","date":"2022-10-07","arxiv_id":"2210.03801","repositories_listed":1,"syntology":null},{"url":"/paper/empowering-graph-representation-learning-with-1","slug":"empowering-graph-representation-learning-with-1","title":"Empowering Graph Representation Learning with Test-Time Graph Transformation","date":"2022-10-07","arxiv_id":"2210.03561","repositories_listed":1,"syntology":null},{"url":"/paper/set2box-similarity-preserving-representation","slug":"set2box-similarity-preserving-representation","title":"Set2Box: Similarity Preserving Representation Learning of Sets","date":"2022-10-07","arxiv_id":"2210.03282","repositories_listed":1,"syntology":null},{"url":"/paper/svl-adapter-self-supervised-adapter-for","slug":"svl-adapter-self-supervised-adapter-for","title":"SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models","date":"2022-10-07","arxiv_id":"2210.03794","repositories_listed":1,"syntology":null},{"url":"/paper/domain-specific-word-embeddings-with","slug":"domain-specific-word-embeddings-with","title":"Domain-Specific Word Embeddings with Structure Prediction","date":"2022-10-06","arxiv_id":"2210.04962","repositories_listed":1,"syntology":null},{"url":"/paper/expander-graph-propagation","slug":"expander-graph-propagation","title":"Expander Graph Propagation","date":"2022-10-06","arxiv_id":"2210.02997","repositories_listed":1,"syntology":null},{"url":"/paper/geodesic-graph-neural-network-for-efficient","slug":"geodesic-graph-neural-network-for-efficient","title":"Geodesic Graph Neural Network for Efficient Graph Representation Learning","date":"2022-10-06","arxiv_id":"2210.02636","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":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) · 4 unverified","sample_list":"/paper/geodesic-graph-neural-network-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2210.02636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02636"}},"official":{"repos":["woodcutter1998/gdgnn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/mechretro-is-a-chemical-mechanism-driven","slug":"mechretro-is-a-chemical-mechanism-driven","title":"MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning","date":"2022-10-06","arxiv_id":"2210.02630","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-self-supervised-graph-neural","slug":"multi-task-self-supervised-graph-neural","title":"Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization","date":"2022-10-05","arxiv_id":"2210.02016","repositories_listed":1,"syntology":null}],"record_sha256":"4c5061dd0de6b7cbc4a0bf78516b492db7cd65580adc318d6c52ca1b06ee64f2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}