{"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/attribute/papers/14","list_of":"/task/attribute","task":"Attribute","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":14,"pages_in_order":54,"rows_per_page":100,"rows":[1301,1400],"of":5387,"counts":{"archive_papers_tagged":5387,"with_a_code_link":1923,"where_syntology_ran_a_sample":475,"not_listed_spam_title":0,"listed":5387,"listed_where_code_ran":475,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":387,"every_run_a_failure_of_syntologys_instrument":88,"listed_with_a_run_with_no_instrument_failure":387,"listed_every_run_a_failure_of_syntologys_instrument":88,"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/attribute","prev":"/task/attribute/papers/13","next":"/task/attribute/papers/15","papers":[{"url":"/paper/migrating-face-swap-to-mobile-devices-a","slug":"migrating-face-swap-to-mobile-devices-a","title":"Migrating Face Swap to Mobile Devices: A lightweight Framework and A Supervised Training Solution","date":"2022-04-13","arxiv_id":"2204.08339","repositories_listed":1,"syntology":null},{"url":"/paper/physically-disentangled-representations","slug":"physically-disentangled-representations","title":"Physically Disentangled Representations","date":"2022-04-11","arxiv_id":"2204.05281","repositories_listed":1,"syntology":null},{"url":"/paper/fashionformer-a-simple-effective-and-unified","slug":"fashionformer-a-simple-effective-and-unified","title":"Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition","date":"2022-04-10","arxiv_id":"2204.04654","repositories_listed":1,"syntology":null},{"url":"/paper/are-two-heads-the-same-as-one-identifying","slug":"are-two-heads-the-same-as-one-identifying","title":"Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks","date":"2022-04-09","arxiv_id":"2204.04440","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/are-two-heads-the-same-as-one-identifying#ran","syntology_url":"https://syntology.ai/paper/2204.04440","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.04440"}},"official":{"repos":["mlohaus/disparatetreatment"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/visible-thermal-uav-tracking-a-large-scale","slug":"visible-thermal-uav-tracking-a-large-scale","title":"Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline","date":"2022-04-08","arxiv_id":"2204.04120","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-compose-soft-prompts-for","slug":"learning-to-compose-soft-prompts-for","title":"Learning to Compose Soft Prompts for Compositional Zero-Shot Learning","date":"2022-04-07","arxiv_id":"2204.03574","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-to-compose-soft-prompts-for#ran","syntology_url":"https://syntology.ai/paper/2204.03574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.03574"}},"official":{"repos":["batsresearch/csp"],"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/predicting-solar-flares-using-cnn-and-lstm-on","slug":"predicting-solar-flares-using-cnn-and-lstm-on","title":"Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data","date":"2022-04-07","arxiv_id":"2204.03710","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-tensor-based-point-cloud-attribute","slug":"sparse-tensor-based-point-cloud-attribute","title":"Sparse Tensor-based Point Cloud Attribute Compression","date":"2022-04-03","arxiv_id":"2204.01023","repositories_listed":1,"syntology":null},{"url":"/paper/do-learned-representations-respect-causal","slug":"do-learned-representations-respect-causal","title":"Do learned representations respect causal relationships?","date":"2022-04-02","arxiv_id":"2204.00762","repositories_listed":1,"syntology":null},{"url":"/paper/fashion-style-generation-evolutionary-search","slug":"fashion-style-generation-evolutionary-search","title":"Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space","date":"2022-04-01","arxiv_id":"2204.00592","repositories_listed":1,"syntology":null},{"url":"/paper/separate-and-conquer-heuristic-allows-robust","slug":"separate-and-conquer-heuristic-allows-robust","title":"Separate and conquer heuristic allows robust mining of contrast sets in classification, regression, and survival data","date":"2022-04-01","arxiv_id":"2204.00497","repositories_listed":1,"syntology":null},{"url":"/paper/transeditor-transformer-based-dual-space-gan","slug":"transeditor-transformer-based-dual-space-gan","title":"TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing","date":"2022-03-31","arxiv_id":"2203.17266","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":11,"n_instrument":3,"n_unverified":1,"n_honours":2,"n_violates":1,"n_no_contract":8,"n_pointer_only":2,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 1 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/transeditor-transformer-based-dual-space-gan#ran","syntology_url":"https://syntology.ai/paper/2203.17266","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.17266"}},"official":{"repos":["billyxyb/transeditor"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fair-contrastive-learning-for-facial","slug":"fair-contrastive-learning-for-facial","title":"Fair Contrastive Learning for Facial Attribute Classification","date":"2022-03-30","arxiv_id":"2203.16209","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":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/fair-contrastive-learning-for-facial#ran","syntology_url":"https://syntology.ai/paper/2203.16209","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16209"}},"official":{"repos":["sungho-coolg/fscl"],"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-fair-models-without-sensitive","slug":"learning-fair-models-without-sensitive","title":"Learning Fair Models without Sensitive Attributes: A Generative Approach","date":"2022-03-30","arxiv_id":"2203.16413","repositories_listed":1,"syntology":null},{"url":"/paper/3d-shape-reconstruction-from-2d-images-with","slug":"3d-shape-reconstruction-from-2d-images-with","title":"3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow","date":"2022-03-29","arxiv_id":"2203.15190","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/3d-shape-reconstruction-from-2d-images-with#ran","syntology_url":"https://syntology.ai/paper/2203.15190","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15190"}},"official":{"repos":["junshengzhou/3dattriflow"],"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/quantifying-societal-bias-amplification-in","slug":"quantifying-societal-bias-amplification-in","title":"Quantifying Societal Bias Amplification in Image Captioning","date":"2022-03-29","arxiv_id":"2203.15395","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/quantifying-societal-bias-amplification-in#ran","syntology_url":"https://syntology.ai/paper/2203.15395","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15395"}},"official":{"repos":["rebnej/lick-caption-bias"],"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/stylet2i-toward-compositional-and-high","slug":"stylet2i-toward-compositional-and-high","title":"StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis","date":"2022-03-29","arxiv_id":"2203.15799","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stylet2i-toward-compositional-and-high#ran","syntology_url":"https://syntology.ai/paper/2203.15799","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15799"}},"official":{"repos":["zhihengli-UR/StyleT2I"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/effective-explanations-for-entity-resolution","slug":"effective-explanations-for-entity-resolution","title":"Effective Explanations for Entity Resolution Models","date":"2022-03-24","arxiv_id":"2203.12978","repositories_listed":1,"syntology":null},{"url":"/paper/ia-faces-bidirectional-method-disentangled","slug":"ia-faces-bidirectional-method-disentangled","title":"IA-FaceS: A Bidirectional Method for Semantic Face Editing","date":"2022-03-24","arxiv_id":"2203.13097","repositories_listed":1,"syntology":null},{"url":"/paper/learning-disentangled-representation-for-one","slug":"learning-disentangled-representation-for-one","title":"Learning Disentangled Representation for One-shot Progressive Face Swapping","date":"2022-03-24","arxiv_id":"2203.12985","repositories_listed":1,"syntology":null},{"url":"/paper/linking-emergent-and-natural-languages-via-1","slug":"linking-emergent-and-natural-languages-via-1","title":"Linking Emergent and Natural Languages via Corpus Transfer","date":"2022-03-24","arxiv_id":"2203.13344","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/linking-emergent-and-natural-languages-via-1#ran","syntology_url":"https://syntology.ai/paper/2203.13344","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13344"}},"official":{"repos":["ysymyth/ec-nl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mix-and-match-learning-free-controllable-text-2","slug":"mix-and-match-learning-free-controllable-text-2","title":"Mix and Match: Learning-free Controllable Text Generation using Energy Language Models","date":"2022-03-24","arxiv_id":"2203.13299","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-censor-by-noisy-sampling","slug":"learning-to-censor-by-noisy-sampling","title":"Learning to Censor by Noisy Sampling","date":"2022-03-23","arxiv_id":"2203.12192","repositories_listed":1,"syntology":null},{"url":"/paper/a-contrastive-objective-for-learning","slug":"a-contrastive-objective-for-learning","title":"A Contrastive Objective for Learning Disentangled Representations","date":"2022-03-21","arxiv_id":"2203.11284","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":1,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-contrastive-objective-for-learning#ran","syntology_url":"https://syntology.ai/paper/2203.11284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11284"}},"official":{"repos":["jonkahana/dcodr"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/attri-vae-attribute-based-disentangled-and","slug":"attri-vae-attribute-based-disentangled-and","title":"Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoders","date":"2022-03-20","arxiv_id":"2203.10417","repositories_listed":1,"syntology":null},{"url":"/paper/3dac-learning-attribute-compression-for-point","slug":"3dac-learning-attribute-compression-for-point","title":"3DAC: Learning Attribute Compression for Point Clouds","date":"2022-03-17","arxiv_id":"2203.09931","repositories_listed":1,"syntology":null},{"url":"/paper/attribute-surrogates-learning-and-spectral","slug":"attribute-surrogates-learning-and-spectral","title":"Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning","date":"2022-03-17","arxiv_id":"2203.09064","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":1,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/attribute-surrogates-learning-and-spectral#ran","syntology_url":"https://syntology.ai/paper/2203.09064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09064"}},"official":{"repos":["stomachcold/hctransformers"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/knowledge-graph-embedding-methods-for-entity","slug":"knowledge-graph-embedding-methods-for-entity","title":"Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review","date":"2022-03-17","arxiv_id":"2203.09280","repositories_listed":1,"syntology":null},{"url":"/paper/sticc-a-multivariate-spatial-clustering","slug":"sticc-a-multivariate-spatial-clustering","title":"STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity","date":"2022-03-17","arxiv_id":"2203.09611","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":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) · 1 unverified","sample_list":"/paper/sticc-a-multivariate-spatial-clustering#ran","syntology_url":"https://syntology.ai/paper/2203.09611","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09611"}},"official":{"repos":["geods/sticc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/attribute-group-editing-for-reliable-few-shot","slug":"attribute-group-editing-for-reliable-few-shot","title":"Attribute Group Editing for Reliable Few-shot Image Generation","date":"2022-03-16","arxiv_id":"2203.08422","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":2,"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/attribute-group-editing-for-reliable-few-shot#ran","syntology_url":"https://syntology.ai/paper/2203.08422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08422"}},"official":{"repos":["unibester/age"],"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/gold-doesn-t-always-glitter-spectral-removal","slug":"gold-doesn-t-always-glitter-spectral-removal","title":"Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information","date":"2022-03-15","arxiv_id":"2203.07893","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/gold-doesn-t-always-glitter-spectral-removal#ran","syntology_url":"https://syntology.ai/paper/2203.07893","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07893"}},"official":{"repos":["jasonshaoshun/SAL"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/style-transformer-for-image-inversion-and","slug":"style-transformer-for-image-inversion-and","title":"Style Transformer for Image Inversion and Editing","date":"2022-03-15","arxiv_id":"2203.07932","repositories_listed":1,"syntology":null},{"url":"/paper/adas-a-direct-adaptation-strategy-for-multi","slug":"adas-a-direct-adaptation-strategy-for-multi","title":"ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation","date":"2022-03-14","arxiv_id":"2203.06811","repositories_listed":1,"syntology":null},{"url":"/paper/deep-continuous-prompt-for-contrastive-1","slug":"deep-continuous-prompt-for-contrastive-1","title":"Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning","date":"2022-03-14","arxiv_id":"2203.06875","repositories_listed":1,"syntology":null},{"url":"/paper/a-gating-model-for-bias-calibration-in","slug":"a-gating-model-for-bias-calibration-in","title":"A Gating Model for Bias Calibration in Generalized Zero-shot Learning","date":"2022-03-08","arxiv_id":"2203.04195","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-framework-of-medical-information","slug":"a-unified-framework-of-medical-information","title":"A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text","date":"2022-03-08","arxiv_id":"2203.03823","repositories_listed":1,"syntology":null},{"url":"/paper/dumlp-pin-a-dual-mlp-dot-product-permutation","slug":"dumlp-pin-a-dual-mlp-dot-product-permutation","title":"DuMLP-Pin: A Dual-MLP-dot-product Permutation-invariant Network for Set Feature Extraction","date":"2022-03-08","arxiv_id":"2203.04007","repositories_listed":1,"syntology":null},{"url":"/paper/glidenet-global-local-and-intrinsic-based","slug":"glidenet-global-local-and-intrinsic-based","title":"GlideNet: Global, Local and Intrinsic based Dense Embedding NETwork for Multi-category Attributes Prediction","date":"2022-03-07","arxiv_id":"2203.03079","repositories_listed":1,"syntology":null},{"url":"/paper/training-privacy-preserving-video-analytics","slug":"training-privacy-preserving-video-analytics","title":"Training privacy-preserving video analytics pipelines by suppressing features that reveal information about private attributes","date":"2022-03-05","arxiv_id":"2203.02635","repositories_listed":1,"syntology":null},{"url":"/paper/correct-n-contrast-a-contrastive-approach-for-1","slug":"correct-n-contrast-a-contrastive-approach-for-1","title":"Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations","date":"2022-03-03","arxiv_id":"2203.01517","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"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 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/correct-n-contrast-a-contrastive-approach-for-1#ran","syntology_url":"https://syntology.ai/paper/2203.01517","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01517"}},"official":{"repos":["HazyResearch/correct-n-contrast"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-effective-graph-learning-based-approach","slug":"an-effective-graph-learning-based-approach","title":"An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022","date":"2022-03-01","arxiv_id":"2203.01820","repositories_listed":1,"syntology":null},{"url":"/paper/deepfake-network-architecture-attribution","slug":"deepfake-network-architecture-attribution","title":"Deepfake Network Architecture Attribution","date":"2022-02-28","arxiv_id":"2202.13843","repositories_listed":1,"syntology":null},{"url":"/paper/robust-textual-embedding-against-word-level","slug":"robust-textual-embedding-against-word-level","title":"Robust Textual Embedding against Word-level Adversarial Attacks","date":"2022-02-28","arxiv_id":"2202.13817","repositories_listed":1,"syntology":null},{"url":"/paper/model-attribution-of-face-swap-deepfake","slug":"model-attribution-of-face-swap-deepfake","title":"Model Attribution of Face-swap Deepfake Videos","date":"2022-02-25","arxiv_id":"2202.12951","repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-texture-information-into","slug":"incorporating-texture-information-into","title":"Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images","date":"2022-02-18","arxiv_id":"2202.09179","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-missing-values-approaches-for","slug":"benchmarking-missing-values-approaches-for","title":"Benchmarking missing-values approaches for predictive models on health databases","date":"2022-02-17","arxiv_id":"2202.10580","repositories_listed":1,"syntology":null},{"url":"/paper/contextual-importance-and-utility","slug":"contextual-importance-and-utility","title":"Contextual Importance and Utility: aTheoretical Foundation","date":"2022-02-15","arxiv_id":"2202.07292","repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-repeatability-of-deep-learning","slug":"improving-the-repeatability-of-deep-learning","title":"Improving the repeatability of deep learning models with Monte Carlo dropout","date":"2022-02-15","arxiv_id":"2202.07562","repositories_listed":1,"syntology":null},{"url":"/paper/diverse-facial-inpainting-guided-by-exemplars","slug":"diverse-facial-inpainting-guided-by-exemplars","title":"Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars","date":"2022-02-13","arxiv_id":"2202.06358","repositories_listed":1,"syntology":null},{"url":"/paper/text-and-image-guided-3d-avatar-generation","slug":"text-and-image-guided-3d-avatar-generation","title":"Text and Image Guided 3D Avatar Generation and Manipulation","date":"2022-02-12","arxiv_id":"2202.06079","repositories_listed":1,"syntology":null},{"url":"/paper/tiny-object-tracking-a-large-scale-dataset","slug":"tiny-object-tracking-a-large-scale-dataset","title":"Tiny Object Tracking: A Large-scale Dataset and A Baseline","date":"2022-02-11","arxiv_id":"2202.05659","repositories_listed":1,"syntology":null},{"url":"/paper/resource-management-and-security-scheme-of","slug":"resource-management-and-security-scheme-of","title":"Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm","date":"2022-02-03","arxiv_id":"2202.01375","repositories_listed":1,"syntology":null},{"url":"/paper/learning-fair-representations-via-rate","slug":"learning-fair-representations-via-rate","title":"Learning Fair Representations via Rate-Distortion Maximization","date":"2022-01-31","arxiv_id":"2202.00035","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-fair-representations-via-rate#ran","syntology_url":"https://syntology.ai/paper/2202.00035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.00035"}},"official":{"repos":["brcsomnath/farm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/explaining-graph-level-predictions-with","slug":"explaining-graph-level-predictions-with","title":"GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games","date":"2022-01-28","arxiv_id":"2201.12380","repositories_listed":1,"syntology":null},{"url":"/paper/dom-lm-learning-generalizable-representations","slug":"dom-lm-learning-generalizable-representations","title":"DOM-LM: Learning Generalizable Representations for HTML Documents","date":"2022-01-25","arxiv_id":"2201.10608","repositories_listed":1,"syntology":null},{"url":"/paper/faceocc-a-diverse-high-quality-face-occlusion","slug":"faceocc-a-diverse-high-quality-face-occlusion","title":"FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction","date":"2022-01-20","arxiv_id":"2201.08425","repositories_listed":1,"syntology":null},{"url":"/paper/a-taxonomy-of-information-attributes-for-test","slug":"a-taxonomy-of-information-attributes-for-test","title":"A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning","date":"2022-01-16","arxiv_id":"2201.06044","repositories_listed":1,"syntology":null},{"url":"/paper/deepke-a-deep-learning-based-knowledge","slug":"deepke-a-deep-learning-based-knowledge","title":"DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population","date":"2022-01-10","arxiv_id":"2201.03335","repositories_listed":1,"syntology":null},{"url":"/paper/ep-pqm-efficient-parametric-probabilistic","slug":"ep-pqm-efficient-parametric-probabilistic","title":"EP-PQM: Efficient Parametric Probabilistic Quantum Memory with Fewer Qubits and Gates","date":"2022-01-10","arxiv_id":"2201.07265","repositories_listed":1,"syntology":null},{"url":"/paper/learning-fair-node-representations-with-graph","slug":"learning-fair-node-representations-with-graph","title":"Learning Fair Node Representations with Graph Counterfactual Fairness","date":"2022-01-10","arxiv_id":"2201.03662","repositories_listed":1,"syntology":null},{"url":"/paper/maskmtl-attribute-prediction-in-masked-facial","slug":"maskmtl-attribute-prediction-in-masked-facial","title":"MaskMTL: Attribute prediction in masked facial images with deep multitask learning","date":"2022-01-09","arxiv_id":"2201.03002","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-chinese-text-recognition","slug":"benchmarking-chinese-text-recognition","title":"Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study","date":"2021-12-30","arxiv_id":"2112.15093","repositories_listed":1,"syntology":null},{"url":"/paper/delving-into-sample-loss-curve-to-embrace","slug":"delving-into-sample-loss-curve-to-embrace","title":"Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data","date":"2021-12-30","arxiv_id":"2201.00849","repositories_listed":1,"syntology":null},{"url":"/paper/2112-14754","slug":"2112-14754","title":"Disentanglement and Generalization Under Correlation Shifts","date":"2021-12-29","arxiv_id":"2112.14754","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/2112-14754#ran","syntology_url":"https://syntology.ai/paper/2112.14754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.14754"}},"official":{"repos":["asteroidhouse/conditional-disentanglement"],"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/attribute-inference-attack-of-speech-emotion","slug":"attribute-inference-attack-of-speech-emotion","title":"Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings","date":"2021-12-26","arxiv_id":"2112.13416","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-generative-zero-shot-learning-by","slug":"boosting-generative-zero-shot-learning-by","title":"Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentation","date":"2021-12-23","arxiv_id":"2112.12573","repositories_listed":1,"syntology":null},{"url":"/paper/multi-choice-questions-based-multi-interest","slug":"multi-choice-questions-based-multi-interest","title":"Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation","date":"2021-12-22","arxiv_id":"2112.11775","repositories_listed":1,"syntology":null},{"url":"/paper/extending-clip-for-category-to-image","slug":"extending-clip-for-category-to-image","title":"Extending CLIP for Category-to-image Retrieval in E-commerce","date":"2021-12-21","arxiv_id":"2112.11294","repositories_listed":1,"syntology":null},{"url":"/paper/initiative-defense-against-facial","slug":"initiative-defense-against-facial","title":"Initiative Defense against Facial Manipulation","date":"2021-12-19","arxiv_id":"2112.10098","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/initiative-defense-against-facial#ran","syntology_url":"https://syntology.ai/paper/2112.10098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.10098"}},"official":{"repos":["shikiw/initiative-defense-for-deepfake"],"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","unlocated"]}}},{"url":"/paper/information-theoretic-stochastic-contrastive-1","slug":"information-theoretic-stochastic-contrastive-1","title":"Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN","date":"2021-12-17","arxiv_id":"2112.09653","repositories_listed":1,"syntology":null},{"url":"/paper/dataset-correlation-inference-attacks-against","slug":"dataset-correlation-inference-attacks-against","title":"Correlation inference attacks against machine learning models","date":"2021-12-16","arxiv_id":"2112.08806","repositories_listed":1,"syntology":null},{"url":"/paper/evidentiality-guided-generation-for-knowledge","slug":"evidentiality-guided-generation-for-knowledge","title":"Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks","date":"2021-12-16","arxiv_id":"2112.08688","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":7,"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) · 3 unverified","sample_list":"/paper/evidentiality-guided-generation-for-knowledge#ran","syntology_url":"https://syntology.ai/paper/2112.08688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.08688"}},"official":{"repos":["akariasai/evidentiality_qa"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/mave-a-product-dataset-for-multi-source","slug":"mave-a-product-dataset-for-multi-source","title":"MAVE: A Product Dataset for Multi-source Attribute Value Extraction","date":"2021-12-16","arxiv_id":"2112.08663","repositories_listed":1,"syntology":null},{"url":"/paper/transzero-cross-attribute-guided-transformer","slug":"transzero-cross-attribute-guided-transformer","title":"TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning","date":"2021-12-16","arxiv_id":"2112.08643","repositories_listed":1,"syntology":null},{"url":"/paper/sgml-a-symmetric-graph-metric-learning","slug":"sgml-a-symmetric-graph-metric-learning","title":"SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification","date":"2021-12-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/measuring-fairness-with-biased-rulers-a","slug":"measuring-fairness-with-biased-rulers-a","title":"Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Models","date":"2021-12-14","arxiv_id":"2112.07447","repositories_listed":1,"syntology":null},{"url":"/paper/shaping-visual-representations-with","slug":"shaping-visual-representations-with","title":"Shaping Visual Representations with Attributes for Few-Shot Recognition","date":"2021-12-13","arxiv_id":"2112.06398","repositories_listed":1,"syntology":null},{"url":"/paper/why-are-you-weird-infusing-interpretability","slug":"why-are-you-weird-infusing-interpretability","title":"Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection","date":"2021-12-13","arxiv_id":"2112.06858","repositories_listed":1,"syntology":null},{"url":"/paper/hairclip-design-your-hair-by-text-and","slug":"hairclip-design-your-hair-by-text-and","title":"HairCLIP: Design Your Hair by Text and Reference Image","date":"2021-12-09","arxiv_id":"2112.05142","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hairclip-design-your-hair-by-text-and#ran","syntology_url":"https://syntology.ai/paper/2112.05142","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.05142"}},"official":{"repos":["wty-ustc/hairclip"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/conerf-controllable-neural-radiance-fields","slug":"conerf-controllable-neural-radiance-fields","title":"CoNeRF: Controllable Neural Radiance Fields","date":"2021-12-03","arxiv_id":"2112.01983","repositories_listed":1,"syntology":{"n":21,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":18,"n_pointer_only":0,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/conerf-controllable-neural-radiance-fields#ran","syntology_url":"https://syntology.ai/paper/2112.01983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.01983"}},"official":{"repos":["kacperkan/conerf"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":18,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/transzero-attribute-guided-transformer-for","slug":"transzero-attribute-guided-transformer-for","title":"TransZero: Attribute-guided Transformer for Zero-Shot Learning","date":"2021-12-03","arxiv_id":"2112.01683","repositories_listed":1,"syntology":null},{"url":"/paper/a-2-net-learning-attribute-aware-hash-codes","slug":"a-2-net-learning-attribute-aware-hash-codes","title":"A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval","date":"2021-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/conditional-generation-using-polynomial","slug":"conditional-generation-using-polynomial","title":"Conditional Generation Using Polynomial Expansions","date":"2021-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improving-gan-equilibrium-by-raising-spatial","slug":"improving-gan-equilibrium-by-raising-spatial","title":"Improving GAN Equilibrium by Raising Spatial Awareness","date":"2021-12-01","arxiv_id":"2112.00718","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-bandits-with-groups-of-similar","slug":"stochastic-bandits-with-groups-of-similar","title":"Stochastic bandits with groups of similar arms.","date":"2021-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/latent-transformations-via-neuralodes-for-gan-1","slug":"latent-transformations-via-neuralodes-for-gan-1","title":"Latent Transformations via NeuralODEs for GAN-based Image Editing","date":"2021-11-29","arxiv_id":"2111.14825","repositories_listed":1,"syntology":null},{"url":"/paper/an-entropy-weighted-nonnegative-matrix","slug":"an-entropy-weighted-nonnegative-matrix","title":"An Entropy Weighted Nonnegative Matrix Factorization Algorithm for Feature Representation","date":"2021-11-27","arxiv_id":"2111.14007","repositories_listed":1,"syntology":null},{"url":"/paper/learning-long-term-reward-redistribution-via-1","slug":"learning-long-term-reward-redistribution-via-1","title":"Learning Long-Term Reward Redistribution via Randomized Return Decomposition","date":"2021-11-26","arxiv_id":"2111.13485","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-long-term-reward-redistribution-via-1#ran","syntology_url":"https://syntology.ai/paper/2111.13485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.13485"}},"official":{"repos":["stilwell-git/randomized-return-decomposition"],"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/attribute-specific-control-units-in-stylegan","slug":"attribute-specific-control-units-in-stylegan","title":"Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation","date":"2021-11-25","arxiv_id":"2111.13010","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/attribute-specific-control-units-in-stylegan#ran","syntology_url":"https://syntology.ai/paper/2111.13010","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.13010"}},"official":{"repos":["budui/Control-Units-in-StyleGAN2"],"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/towards-practical-deployment-stage-backdoor","slug":"towards-practical-deployment-stage-backdoor","title":"Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks","date":"2021-11-25","arxiv_id":"2111.12965","repositories_listed":1,"syntology":null},{"url":"/paper/unbiased-pairwise-learning-to-rank-in","slug":"unbiased-pairwise-learning-to-rank-in","title":"Unbiased Pairwise Learning to Rank in Recommender Systems","date":"2021-11-25","arxiv_id":"2111.12929","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-feature-ranking-via-attribute","slug":"unsupervised-feature-ranking-via-attribute","title":"Unsupervised Feature Ranking via Attribute Networks","date":"2021-11-25","arxiv_id":"2111.13273","repositories_listed":1,"syntology":null},{"url":"/paper/cerberus-transformer-joint-semantic","slug":"cerberus-transformer-joint-semantic","title":"Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing","date":"2021-11-24","arxiv_id":"2111.12608","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-training-data-leakage-from","slug":"understanding-training-data-leakage-from","title":"Understanding Training-Data Leakage from Gradients in Neural Networks for Image Classification","date":"2021-11-19","arxiv_id":"2111.10178","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-effectiveness-of-sparsification-for","slug":"on-the-effectiveness-of-sparsification-for","title":"DICE: Leveraging Sparsification for Out-of-Distribution Detection","date":"2021-11-18","arxiv_id":"2111.09805","repositories_listed":1,"syntology":{"n":19,"n_ran":17,"n_constructed":0,"n_ran_checked":10,"n_instrument":7,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":2,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 7 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/on-the-effectiveness-of-sparsification-for#ran","syntology_url":"https://syntology.ai/paper/2111.09805","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.09805"}},"official":{"repos":["deeplearning-wisc/dice"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/lvac-learned-volumetric-attribute-compression","slug":"lvac-learned-volumetric-attribute-compression","title":"LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks","date":"2021-11-17","arxiv_id":"2111.08988","repositories_listed":1,"syntology":null},{"url":"/paper/multi-attribute-relation-extraction-mare","slug":"multi-attribute-relation-extraction-mare","title":"Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction","date":"2021-11-17","arxiv_id":"2111.09035","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-high-fidelity-and-re","slug":"self-supervised-high-fidelity-and-re","title":"Self-supervised Re-renderable Facial Albedo Reconstruction from Single Image","date":"2021-11-16","arxiv_id":"2111.08282","repositories_listed":1,"syntology":null},{"url":"/paper/property-inference-attacks-against-gans","slug":"property-inference-attacks-against-gans","title":"Property Inference Attacks Against GANs","date":"2021-11-15","arxiv_id":"2111.07608","repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-attribute-controllable-generative","slug":"a-multi-attribute-controllable-generative","title":"A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis","date":"2021-11-10","arxiv_id":"2111.06398","repositories_listed":1,"syntology":null}],"record_sha256":"10c04d4d02c3d1666bb25661fd4ebba96871966e9667f584e08d27a1ba91ffcd","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}