{"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/inductive-bias/papers/2","list_of":"/task/inductive-bias","task":"Inductive Bias","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":2,"pages_in_order":16,"rows_per_page":100,"rows":[101,200],"of":1529,"counts":{"archive_papers_tagged":1529,"with_a_code_link":716,"where_syntology_ran_a_sample":284,"not_listed_spam_title":0,"listed":1529,"listed_where_code_ran":284,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":244,"every_run_a_failure_of_syntologys_instrument":40,"listed_with_a_run_with_no_instrument_failure":244,"listed_every_run_a_failure_of_syntologys_instrument":40,"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/inductive-bias","prev":"/task/inductive-bias","next":"/task/inductive-bias/papers/3","papers":[{"url":"/paper/container-context-aggregation-networks","slug":"container-context-aggregation-networks","title":"Container: Context Aggregation Networks","date":"2021-12-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/end-to-end-referring-video-object","slug":"end-to-end-referring-video-object","title":"End-to-End Referring Video Object Segmentation with Multimodal Transformers","date":"2021-11-29","arxiv_id":"2111.14821","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":4,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/end-to-end-referring-video-object#ran","syntology_url":"https://syntology.ai/paper/2111.14821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.14821"}},"official":{"repos":["mttr2021/MTTR"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-foreground-extraction-via-deep","slug":"unsupervised-foreground-extraction-via-deep","title":"Unsupervised Foreground Extraction via Deep Region Competition","date":"2021-10-29","arxiv_id":"2110.15497","repositories_listed":2,"syntology":{"n":15,"n_ran":13,"n_constructed":10,"n_ran_checked":12,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":15,"phrase":"13 ran (of which 10 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-foreground-extraction-via-deep#ran","syntology_url":"https://syntology.ai/paper/2110.15497","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.15497"}},"official":{"repos":["yupeiyu98/deep-region-competition","yupeiyu98/drc"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":10,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mvt-multi-view-vision-transformer-for-3d","slug":"mvt-multi-view-vision-transformer-for-3d","title":"MVT: Multi-view Vision Transformer for 3D Object Recognition","date":"2021-10-25","arxiv_id":"2110.13083","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mvt-multi-view-vision-transformer-for-3d#ran","syntology_url":"https://syntology.ai/paper/2110.13083","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.13083"}},"official":{"repos":["shanshuo/MVT"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/nnformer-interleaved-transformer-for","slug":"nnformer-interleaved-transformer-for","title":"nnFormer: Interleaved Transformer for Volumetric Segmentation","date":"2021-09-07","arxiv_id":"2109.03201","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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/nnformer-interleaved-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2109.03201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.03201"}},"official":{"repos":["282857341/nnformer"],"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/raftmlp-do-mlp-based-models-dream-of-winning","slug":"raftmlp-do-mlp-based-models-dream-of-winning","title":"RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?","date":"2021-08-09","arxiv_id":"2108.04384","repositories_listed":2,"syntology":null},{"url":"/paper/h-transformer-1d-fast-one-dimensional","slug":"h-transformer-1d-fast-one-dimensional","title":"H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences","date":"2021-07-25","arxiv_id":"2107.11906","repositories_listed":2,"syntology":{"n":10,"n_ran":9,"n_constructed":1,"n_ran_checked":6,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":1,"phrase":"9 ran (of which 1 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/h-transformer-1d-fast-one-dimensional#ran","syntology_url":"https://syntology.ai/paper/2107.11906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.11906"}},"official":null}},{"url":"/paper/vision-xformers-efficient-attention-for-image","slug":"vision-xformers-efficient-attention-for-image","title":"Vision Xformers: Efficient Attention for Image Classification","date":"2021-07-05","arxiv_id":"2107.02239","repositories_listed":2,"syntology":null},{"url":"/paper/the-causal-neural-connection-expressiveness","slug":"the-causal-neural-connection-expressiveness","title":"The Causal-Neural Connection: Expressiveness, Learnability, and Inference","date":"2021-07-02","arxiv_id":"2107.00793","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/the-causal-neural-connection-expressiveness#ran","syntology_url":"https://syntology.ai/paper/2107.00793","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.00793"}},"official":{"repos":["causalailab/neuralcausalmodels"],"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/resvit-residual-vision-transformers-for-multi","slug":"resvit-residual-vision-transformers-for-multi","title":"ResViT: Residual vision transformers for multi-modal medical image synthesis","date":"2021-06-30","arxiv_id":"2106.16031","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/resvit-residual-vision-transformers-for-multi#ran","syntology_url":"https://syntology.ai/paper/2106.16031","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.16031"}},"official":{"repos":["icon-lab/ResViT"],"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/charformer-fast-character-transformers-via","slug":"charformer-fast-character-transformers-via","title":"Charformer: Fast Character Transformers via Gradient-based Subword Tokenization","date":"2021-06-23","arxiv_id":"2106.12672","repositories_listed":2,"syntology":{"n":10,"n_ran":7,"n_constructed":3,"n_ran_checked":4,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"7 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/charformer-fast-character-transformers-via#ran","syntology_url":"https://syntology.ai/paper/2106.12672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.12672"}},"official":{"repos":["google-research/google-research"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/on-inductive-biases-for-heterogeneous","slug":"on-inductive-biases-for-heterogeneous","title":"On Inductive Biases for Heterogeneous Treatment Effect Estimation","date":"2021-06-07","arxiv_id":"2106.03765","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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/on-inductive-biases-for-heterogeneous#ran","syntology_url":"https://syntology.ai/paper/2106.03765","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03765"}},"official":{"repos":["AliciaCurth/CATENets"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/vitae-vision-transformer-advanced-by","slug":"vitae-vision-transformer-advanced-by","title":"ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias","date":"2021-06-07","arxiv_id":"2106.03348","repositories_listed":2,"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/vitae-vision-transformer-advanced-by#ran","syntology_url":"https://syntology.ai/paper/2106.03348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03348"}},"official":{"repos":["Annbless/ViTAE"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/backdoor-attacks-on-self-supervised-learning","slug":"backdoor-attacks-on-self-supervised-learning","title":"Backdoor Attacks on Self-Supervised Learning","date":"2021-05-21","arxiv_id":"2105.10123","repositories_listed":2,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":5,"n_instrument":6,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"phrase":"11 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; 6 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/backdoor-attacks-on-self-supervised-learning#ran","syntology_url":"https://syntology.ai/paper/2105.10123","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.10123"}},"official":{"repos":["UMBCvision/SSL-Backdoor"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/medical-image-segmentation-using-squeeze-and","slug":"medical-image-segmentation-using-squeeze-and","title":"Medical Image Segmentation Using Squeeze-and-Expansion Transformers","date":"2021-05-20","arxiv_id":"2105.09511","repositories_listed":2,"syntology":{"n":20,"n_ran":13,"n_constructed":8,"n_ran_checked":11,"n_instrument":2,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":20,"phrase":"13 ran (of which 8 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/medical-image-segmentation-using-squeeze-and#ran","syntology_url":"https://syntology.ai/paper/2105.09511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.09511"}},"official":{"repos":["askerlee/segtran"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":8,"n_ran_no_instrument_failure":11,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-syn-to-real-generalization-1","slug":"contrastive-syn-to-real-generalization-1","title":"Contrastive Syn-to-Real Generalization","date":"2021-04-06","arxiv_id":"2104.02290","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":1,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/contrastive-syn-to-real-generalization-1#ran","syntology_url":"https://syntology.ai/paper/2104.02290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.02290"}},"official":{"repos":["NVlabs/CSG"],"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":["listed","official"]}}},{"url":"/paper/picie-unsupervised-semantic-segmentation","slug":"picie-unsupervised-semantic-segmentation","title":"PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering","date":"2021-03-30","arxiv_id":"2103.17070","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"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) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/picie-unsupervised-semantic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2103.17070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.17070"}},"official":{"repos":["janghyuncho/PiCIE"],"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":["listed","official"]}}},{"url":"/paper/mc-lstm-mass-conserving-lstm-1","slug":"mc-lstm-mass-conserving-lstm-1","title":"MC-LSTM: Mass-Conserving LSTM","date":"2021-01-13","arxiv_id":"2101.05186","repositories_listed":2,"syntology":null},{"url":"/paper/dynamic-hybrid-relation-network-for-cross","slug":"dynamic-hybrid-relation-network-for-cross","title":"Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing","date":"2021-01-05","arxiv_id":"2101.01686","repositories_listed":2,"syntology":null},{"url":"/paper/neural-anisotropy-directions","slug":"neural-anisotropy-directions","title":"Neural Anisotropy Directions","date":"2020-06-17","arxiv_id":"2006.09717","repositories_listed":2,"syntology":{"n":9,"n_ran":4,"n_constructed":2,"n_ran_checked":2,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 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; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/neural-anisotropy-directions#ran","syntology_url":"https://syntology.ai/paper/2006.09717","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.09717"}},"official":{"repos":["LTS4/neural-anisotropy-directions"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/how-interpretable-and-trustworthy-are-gams","slug":"how-interpretable-and-trustworthy-are-gams","title":"How Interpretable and Trustworthy are GAMs?","date":"2020-06-11","arxiv_id":"2006.06466","repositories_listed":2,"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/how-interpretable-and-trustworthy-are-gams#ran","syntology_url":"https://syntology.ai/paper/2006.06466","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06466"}},"official":{"repos":["zzzace2000/GAMs"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/expbert-representation-engineering-with","slug":"expbert-representation-engineering-with","title":"ExpBERT: Representation Engineering with Natural Language Explanations","date":"2020-05-05","arxiv_id":"2005.01932","repositories_listed":2,"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/expbert-representation-engineering-with#ran","syntology_url":"https://syntology.ai/paper/2005.01932","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.01932"}},"official":{"repos":["MurtyShikhar/ExpBERT","worksheets.codalab.org/worksheets/0x609d2d6a66194592a7f44fbb67ba9f49"],"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/generalizing-convolutional-neural-networks","slug":"generalizing-convolutional-neural-networks","title":"Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data","date":"2020-02-25","arxiv_id":"2002.12880","repositories_listed":2,"syntology":null},{"url":"/paper/an-inductive-bias-for-distances-neural-nets-1","slug":"an-inductive-bias-for-distances-neural-nets-1","title":"An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality","date":"2020-02-14","arxiv_id":"2002.05825","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":3,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-inductive-bias-for-distances-neural-nets-1#ran","syntology_url":"https://syntology.ai/paper/2002.05825","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.05825"}},"official":{"repos":["spitis/deepnorms"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-module-networks-for-reasoning-over-1","slug":"neural-module-networks-for-reasoning-over-1","title":"Neural Module Networks for Reasoning over Text","date":"2019-12-10","arxiv_id":"1912.04971","repositories_listed":2,"syntology":null},{"url":"/paper/on-mutual-information-maximization-for","slug":"on-mutual-information-maximization-for","title":"On Mutual Information Maximization for Representation Learning","date":"2019-07-31","arxiv_id":"1907.13625","repositories_listed":2,"syntology":null},{"url":"/paper/graph-based-knowledge-distillation-by-multi","slug":"graph-based-knowledge-distillation-by-multi","title":"Graph-based Knowledge Distillation by Multi-head Attention Network","date":"2019-07-04","arxiv_id":"1907.02226","repositories_listed":2,"syntology":{"n":18,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":18,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 18 unverified","sample_list":"/paper/graph-based-knowledge-distillation-by-multi#ran","syntology_url":"https://syntology.ai/paper/1907.02226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.02226"}},"official":{"repos":["sseung0703/Knowledge_distillation_via_TF2.0"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"url":"/paper/semantically-conditioned-dialog-response","slug":"semantically-conditioned-dialog-response","title":"Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention","date":"2019-05-30","arxiv_id":"1905.12866","repositories_listed":2,"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":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) · 1 unverified","sample_list":"/paper/semantically-conditioned-dialog-response#ran","syntology_url":"https://syntology.ai/paper/1905.12866","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.12866"}},"official":{"repos":["budzianowski/multiwoz","wenhuchen/HDSA-Dialog"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/exact-hard-monotonic-attention-for-character","slug":"exact-hard-monotonic-attention-for-character","title":"Exact Hard Monotonic Attention for Character-Level Transduction","date":"2019-05-15","arxiv_id":"1905.06319","repositories_listed":2,"syntology":null},{"url":"/paper/neural-3d-morphable-models-spiral","slug":"neural-3d-morphable-models-spiral","title":"Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation","date":"2019-05-08","arxiv_id":"1905.02876","repositories_listed":2,"syntology":null},{"url":"/paper/discretizing-continuous-action-space-for-on","slug":"discretizing-continuous-action-space-for-on","title":"Discretizing Continuous Action Space for On-Policy Optimization","date":"2019-01-29","arxiv_id":"1901.10500","repositories_listed":2,"syntology":null},{"url":"/paper/speech-and-speaker-recognition-from-raw","slug":"speech-and-speaker-recognition-from-raw","title":"Speech and Speaker Recognition from Raw Waveform with SincNet","date":"2018-12-13","arxiv_id":"1812.05920","repositories_listed":2,"syntology":null},{"url":"/paper/auto-encoding-scene-graphs-for-image","slug":"auto-encoding-scene-graphs-for-image","title":"Auto-Encoding Scene Graphs for Image Captioning","date":"2018-12-06","arxiv_id":"1812.02378","repositories_listed":2,"syntology":null},{"url":"/paper/explainable-and-explicit-visual-reasoning","slug":"explainable-and-explicit-visual-reasoning","title":"Explainable and Explicit Visual Reasoning over Scene Graphs","date":"2018-12-05","arxiv_id":"1812.01855","repositories_listed":2,"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/explainable-and-explicit-visual-reasoning#ran","syntology_url":"https://syntology.ai/paper/1812.01855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01855"}},"official":{"repos":["shijx12/XNM-Net"],"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/bias-and-generalization-in-deep-generative","slug":"bias-and-generalization-in-deep-generative","title":"Bias and Generalization in Deep Generative Models: An Empirical Study","date":"2018-11-08","arxiv_id":"1811.03259","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/bias-and-generalization-in-deep-generative#ran","syntology_url":"https://syntology.ai/paper/1811.03259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.03259"}},"official":{"repos":["ermongroup/BiasAndGeneralization"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/compositional-language-understanding-with","slug":"compositional-language-understanding-with","title":"Compositional Language Understanding with Text-based Relational Reasoning","date":"2018-11-07","arxiv_id":"1811.02959","repositories_listed":2,"syntology":null},{"url":"/paper/multitask-learning-a-knowledge-based-source","slug":"multitask-learning-a-knowledge-based-source","title":"Multitask Learning: A Knowledge-Based Source of Inductive Bias","date":"1993-01-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/learning-robust-stereo-matching-in-the-wild","slug":"learning-robust-stereo-matching-in-the-wild","title":"Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts","date":"2025-07-07","arxiv_id":"2507.04631","repositories_listed":1,"syntology":null},{"url":"/paper/agtcnet-a-graph-temporal-approach-for","slug":"agtcnet-a-graph-temporal-approach-for","title":"AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification","date":"2025-06-26","arxiv_id":"2506.21338","repositories_listed":1,"syntology":null},{"url":"/paper/segment-this-thing-foveated-tokenization-for-1","slug":"segment-this-thing-foveated-tokenization-for-1","title":"Segment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation","date":"2025-06-10","arxiv_id":"2506.11131","repositories_listed":1,"syntology":null},{"url":"/paper/parameter-free-approximate-equivariance-for","slug":"parameter-free-approximate-equivariance-for","title":"Parameter-free approximate equivariance for tasks with finite group symmetry","date":"2025-06-09","arxiv_id":"2506.08244","repositories_listed":1,"syntology":null},{"url":"/paper/positional-encoding-meets-persistent-homology","slug":"positional-encoding-meets-persistent-homology","title":"Positional Encoding meets Persistent Homology on Graphs","date":"2025-06-06","arxiv_id":"2506.05814","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/positional-encoding-meets-persistent-homology#ran","syntology_url":"https://syntology.ai/paper/2506.05814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.05814"}},"official":{"repos":["aalto-quml/pipe"],"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/information-locality-as-an-inductive-bias-for","slug":"information-locality-as-an-inductive-bias-for","title":"Information Locality as an Inductive Bias for Neural Language Models","date":"2025-06-05","arxiv_id":"2506.05136","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-cluster-neuronal-function","slug":"learning-to-cluster-neuronal-function","title":"Learning to cluster neuronal function","date":"2025-06-03","arxiv_id":"2506.03293","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/learning-to-cluster-neuronal-function#ran","syntology_url":"https://syntology.ai/paper/2506.03293","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.03293"}},"official":{"repos":["nisone2000/sensorium"],"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/preventing-spurious-interactions-a-new","slug":"preventing-spurious-interactions-a-new","title":"Preventing Spurious Interactions: A New Inductive Bias for Accurate Treatment Effect Estimation","date":"2025-05-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/synthetic-time-series-forecasting-with","slug":"synthetic-time-series-forecasting-with","title":"Synthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks","date":"2025-05-26","arxiv_id":"2505.20048","repositories_listed":1,"syntology":null},{"url":"/paper/polypose-localizing-deformable-anatomy-in-3d","slug":"polypose-localizing-deformable-anatomy-in-3d","title":"PolyPose: Localizing Deformable Anatomy in 3D from Sparse 2D X-ray Images using Polyrigid Transforms","date":"2025-05-25","arxiv_id":"2505.19256","repositories_listed":1,"syntology":null},{"url":"/paper/maximum-total-correlation-reinforcement","slug":"maximum-total-correlation-reinforcement","title":"Maximum Total Correlation Reinforcement Learning","date":"2025-05-22","arxiv_id":"2505.16734","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":6,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":11,"phrase":"8 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/maximum-total-correlation-reinforcement#ran","syntology_url":"https://syntology.ai/paper/2505.16734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.16734"}},"official":{"repos":["bangyou01/mtc"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/stronger-vits-with-octic-equivariance","slug":"stronger-vits-with-octic-equivariance","title":"Stronger ViTs With Octic Equivariance","date":"2025-05-21","arxiv_id":"2505.15441","repositories_listed":1,"syntology":null},{"url":"/paper/a-minimum-description-length-approach-to","slug":"a-minimum-description-length-approach-to","title":"A Minimum Description Length Approach to Regularization in Neural Networks","date":"2025-05-19","arxiv_id":"2505.13398","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-minimum-description-length-approach-to#ran","syntology_url":"https://syntology.ai/paper/2505.13398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.13398"}},"official":{"repos":["taucompling/mdl-reg-approach"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/2505-11157","slug":"2505-11157","title":"Attention on the Sphere","date":"2025-05-16","arxiv_id":"2505.11157","repositories_listed":1,"syntology":null},{"url":"/paper/graph-fourier-transformer-with-structure","slug":"graph-fourier-transformer-with-structure","title":"Graph Fourier Transformer with Structure-Frequency Information","date":"2025-04-28","arxiv_id":"2504.19740","repositories_listed":1,"syntology":null},{"url":"/paper/noisyrollout-reinforcing-visual-reasoning","slug":"noisyrollout-reinforcing-visual-reasoning","title":"NoisyRollout: Reinforcing Visual Reasoning with Data Augmentation","date":"2025-04-17","arxiv_id":"2504.13055","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":5,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","sample_list":"/paper/noisyrollout-reinforcing-visual-reasoning#ran","syntology_url":"https://syntology.ai/paper/2504.13055","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.13055"}},"official":null}},{"url":"/paper/generalization-through-variance-how-noise","slug":"generalization-through-variance-how-noise","title":"Generalization through variance: how noise shapes inductive biases in diffusion models","date":"2025-04-16","arxiv_id":"2504.12532","repositories_listed":1,"syntology":null},{"url":"/paper/cg-tgan-conditional-generative-adversarial","slug":"cg-tgan-conditional-generative-adversarial","title":"CG-TGAN: Conditional Generative Adversarial Networks with Graph Neural Networks for Tabular Data Synthesizing","date":"2025-04-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-reinforcement-learning-via-object","slug":"deep-reinforcement-learning-via-object","title":"Deep Reinforcement Learning via Object-Centric Attention","date":"2025-04-03","arxiv_id":"2504.03024","repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-coupling-knowledge-into-echo","slug":"incorporating-coupling-knowledge-into-echo","title":"Incorporating Coupling Knowledge into Echo State Networks for Learning Spatiotemporally Chaotic Dynamics","date":"2025-04-02","arxiv_id":"2504.01532","repositories_listed":1,"syntology":null},{"url":"/paper/voteflow-enforcing-local-rigidity-in-self","slug":"voteflow-enforcing-local-rigidity-in-self","title":"VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow","date":"2025-03-28","arxiv_id":"2503.22328","repositories_listed":1,"syntology":null},{"url":"/paper/enigmatom-improve-llms-theory-of-mind","slug":"enigmatom-improve-llms-theory-of-mind","title":"EnigmaToM: Improve LLMs' Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity States","date":"2025-03-05","arxiv_id":"2503.03340","repositories_listed":1,"syntology":null},{"url":"/paper/leap-inductive-link-prediction-via-learnable","slug":"leap-inductive-link-prediction-via-learnable","title":"Leap: Inductive Link Prediction via Learnable TopologyAugmentation","date":"2025-03-05","arxiv_id":"2503.03331","repositories_listed":1,"syntology":null},{"url":"/paper/wyckoff-transformer-generation-of-symmetric","slug":"wyckoff-transformer-generation-of-symmetric","title":"Wyckoff Transformer: Generation of Symmetric Crystals","date":"2025-03-04","arxiv_id":"2503.02407","repositories_listed":1,"syntology":null},{"url":"/paper/are-sparse-autoencoders-useful-a-case-study","slug":"are-sparse-autoencoders-useful-a-case-study","title":"Are Sparse Autoencoders Useful? A Case Study in Sparse Probing","date":"2025-02-23","arxiv_id":"2502.16681","repositories_listed":1,"syntology":null},{"url":"/paper/compress-image-to-patches-for-vision","slug":"compress-image-to-patches-for-vision","title":"Compress image to patches for Vision Transformer","date":"2025-02-14","arxiv_id":"2502.10120","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-potential-of-encoder-free","slug":"exploring-the-potential-of-encoder-free","title":"Exploring the Potential of Encoder-free Architectures in 3D LMMs","date":"2025-02-13","arxiv_id":"2502.09620","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/exploring-the-potential-of-encoder-free#ran","syntology_url":"https://syntology.ai/paper/2502.09620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.09620"}},"official":{"repos":["ivan-tang-3d/enel"],"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/data-augmentation-and-regularization-for","slug":"data-augmentation-and-regularization-for","title":"Data Augmentation and Regularization for Learning Group Equivariance","date":"2025-02-10","arxiv_id":"2502.06547","repositories_listed":1,"syntology":null},{"url":"/paper/powerformer-a-transformer-with-weighted","slug":"powerformer-a-transformer-with-weighted","title":"Powerformer: A Transformer with Weighted Causal Attention for Time-series Forecasting","date":"2025-02-10","arxiv_id":"2502.06151","repositories_listed":1,"syntology":null},{"url":"/paper/kronecker-mask-and-interpretive-prompts-are","slug":"kronecker-mask-and-interpretive-prompts-are","title":"Kronecker Mask and Interpretive Prompts are Language-Action Video Learners","date":"2025-02-05","arxiv_id":"2502.03549","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":9,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":11,"phrase":"9 ran (of which 9 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 9 samples that ran constructed an object rather than computing a result","sample_list":"/paper/kronecker-mask-and-interpretive-prompts-are#ran","syntology_url":"https://syntology.ai/paper/2502.03549","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.03549"}},"official":{"repos":["yjyddq/CLAVER"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":9,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/lor-vp-low-rank-visual-prompting-for","slug":"lor-vp-low-rank-visual-prompting-for","title":"LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation","date":"2025-02-02","arxiv_id":"2502.00896","repositories_listed":1,"syntology":null},{"url":"/paper/are-the-latent-representations-of-foundation","slug":"are-the-latent-representations-of-foundation","title":"Are the Latent Representations of Foundation Models for Pathology Invariant to Rotation?","date":"2024-12-16","arxiv_id":"2412.11938","repositories_listed":1,"syntology":null},{"url":"/paper/deep-random-features-for-scalable","slug":"deep-random-features-for-scalable","title":"Deep Random Features for Scalable Interpolation of Spatiotemporal Data","date":"2024-12-16","arxiv_id":"2412.11350","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/deep-random-features-for-scalable#ran","syntology_url":"https://syntology.ai/paper/2412.11350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.11350"}},"official":{"repos":["totony4real/deeprandomfeatures"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-symmetries-via-weight-sharing-with","slug":"learning-symmetries-via-weight-sharing-with","title":"Learning Symmetries via Weight-Sharing with Doubly Stochastic Tensors","date":"2024-12-05","arxiv_id":"2412.04594","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-symmetries-via-weight-sharing-with#ran","syntology_url":"https://syntology.ai/paper/2412.04594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.04594"}},"official":{"repos":["computri/learnable-weight-sharing"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/training-mlps-on-graphs-without-supervision","slug":"training-mlps-on-graphs-without-supervision","title":"Training MLPs on Graphs without Supervision","date":"2024-12-05","arxiv_id":"2412.03864","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/training-mlps-on-graphs-without-supervision#ran","syntology_url":"https://syntology.ai/paper/2412.03864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.03864"}},"official":{"repos":["zehong-wang/simmlp"],"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/object-centric-proto-symbolic-behavioural","slug":"object-centric-proto-symbolic-behavioural","title":"Object-centric proto-symbolic behavioural reasoning from pixels","date":"2024-11-26","arxiv_id":"2411.17438","repositories_listed":1,"syntology":null},{"url":"/paper/faithful-label-free-knowledge-distillation","slug":"faithful-label-free-knowledge-distillation","title":"Faithful Label-free Knowledge Distillation","date":"2024-11-22","arxiv_id":"2411.15239","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-laws-for-task-optimized-models-of-the","slug":"scaling-laws-for-task-optimized-models-of-the","title":"Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream","date":"2024-11-08","arxiv_id":"2411.05712","repositories_listed":1,"syntology":null},{"url":"/paper/dimsum-diffusion-mamba-a-scalable-and-unified","slug":"dimsum-diffusion-mamba-a-scalable-and-unified","title":"DiMSUM: Diffusion Mamba -- A Scalable and Unified Spatial-Frequency Method for Image Generation","date":"2024-11-06","arxiv_id":"2411.04168","repositories_listed":1,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dimsum-diffusion-mamba-a-scalable-and-unified#ran","syntology_url":"https://syntology.ai/paper/2411.04168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.04168"}},"official":{"repos":["vinairesearch/dimsum"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-neural-networks-and-non-commuting","slug":"graph-neural-networks-and-non-commuting","title":"Graph neural networks and non-commuting operators","date":"2024-11-06","arxiv_id":"2411.04265","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":2,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"7 ran (of which 2 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graph-neural-networks-and-non-commuting#ran","syntology_url":"https://syntology.ai/paper/2411.04265","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.04265"}},"official":{"repos":["kkylie/gtnn_weighted_circulant_graphs"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":1,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/learning-general-purpose-biomedical-volume","slug":"learning-general-purpose-biomedical-volume","title":"Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis","date":"2024-11-04","arxiv_id":"2411.02372","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":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) · 1 unverified","sample_list":"/paper/learning-general-purpose-biomedical-volume#ran","syntology_url":"https://syntology.ai/paper/2411.02372","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.02372"}},"official":{"repos":["neel-dey/anatomix"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/regress-don-t-guess-a-regression-like-loss-on","slug":"regress-don-t-guess-a-regression-like-loss-on","title":"Regress, Don't Guess -- A Regression-like Loss on Number Tokens for Language Models","date":"2024-11-04","arxiv_id":"2411.02083","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/regress-don-t-guess-a-regression-like-loss-on#ran","syntology_url":"https://syntology.ai/paper/2411.02083","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.02083"}},"official":{"repos":["tum-ai/number-token-loss"],"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/understanding-generalizability-of-diffusion","slug":"understanding-generalizability-of-diffusion","title":"Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure","date":"2024-10-31","arxiv_id":"2410.24060","repositories_listed":1,"syntology":{"n":20,"n_ran":12,"n_constructed":0,"n_ran_checked":8,"n_instrument":4,"n_unverified":8,"n_honours":3,"n_violates":0,"n_no_contract":5,"n_pointer_only":20,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 3 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/understanding-generalizability-of-diffusion#ran","syntology_url":"https://syntology.ai/paper/2410.24060","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.24060"}},"official":{"repos":["Morefre/Understanding-Generalizability-of-Diffusion-Models-Requires-Rethinking-the-Hidden-Gaussian-Structure"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/on-inductive-biases-that-enable","slug":"on-inductive-biases-that-enable","title":"On Inductive Biases That Enable Generalization of Diffusion Transformers","date":"2024-10-28","arxiv_id":"2410.21273","repositories_listed":1,"syntology":null},{"url":"/paper/habaek-high-performance-water-segmentation","slug":"habaek-high-performance-water-segmentation","title":"Habaek: High-performance water segmentation through dataset expansion and inductive bias optimization","date":"2024-10-21","arxiv_id":"2410.15794","repositories_listed":1,"syntology":null},{"url":"/paper/balancing-label-quantity-and-quality-for","slug":"balancing-label-quantity-and-quality-for","title":"Balancing Label Quantity and Quality for Scalable Elicitation","date":"2024-10-17","arxiv_id":"2410.13215","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/balancing-label-quantity-and-quality-for#ran","syntology_url":"https://syntology.ai/paper/2410.13215","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13215"}},"official":{"repos":["eleutherai/scalable-elicitation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fdf-flexible-decoupled-framework-for-time","slug":"fdf-flexible-decoupled-framework-for-time","title":"FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial Modeling","date":"2024-10-17","arxiv_id":"2410.13253","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/fdf-flexible-decoupled-framework-for-time#ran","syntology_url":"https://syntology.ai/paper/2410.13253","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13253"}},"official":{"repos":["zjt-gpu/fdf"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-dt-offline-meta-rl-as-conditional","slug":"meta-dt-offline-meta-rl-as-conditional","title":"Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement","date":"2024-10-15","arxiv_id":"2410.11448","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"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) · 2 unverified","sample_list":"/paper/meta-dt-offline-meta-rl-as-conditional#ran","syntology_url":"https://syntology.ai/paper/2410.11448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11448"}},"official":{"repos":["nju-rl/meta-dt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/when-does-perceptual-alignment-benefit-vision","slug":"when-does-perceptual-alignment-benefit-vision","title":"When Does Perceptual Alignment Benefit Vision Representations?","date":"2024-10-14","arxiv_id":"2410.10817","repositories_listed":1,"syntology":null},{"url":"/paper/physics-informed-regularization-for-domain","slug":"physics-informed-regularization-for-domain","title":"Physics-Informed Regularization for Domain-Agnostic Dynamical System Modeling","date":"2024-10-08","arxiv_id":"2410.06366","repositories_listed":1,"syntology":{"n":11,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/physics-informed-regularization-for-domain#ran","syntology_url":"https://syntology.ai/paper/2410.06366","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.06366"}},"official":{"repos":["wanjiaZhao1203/TREAT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/unitary-convolutions-for-learning-on-graphs","slug":"unitary-convolutions-for-learning-on-graphs","title":"Unitary convolutions for learning on graphs and groups","date":"2024-10-07","arxiv_id":"2410.05499","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/unitary-convolutions-for-learning-on-graphs#ran","syntology_url":"https://syntology.ai/paper/2410.05499","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.05499"}},"official":{"repos":["Weber-GeoML/Unitary_Convolutions"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-simple-but-strong-baseline-for-sounding","slug":"a-simple-but-strong-baseline-for-sounding","title":"A Simple but Strong Baseline for Sounding Video Generation: Effective Adaptation of Audio and Video Diffusion Models for Joint Generation","date":"2024-09-26","arxiv_id":"2409.17550","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":3,"n_no_contract":1,"n_pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 3 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-simple-but-strong-baseline-for-sounding#ran","syntology_url":"https://syntology.ai/paper/2409.17550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17550"}},"official":{"repos":["sonyresearch/svg_baseline"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/disentanglement-with-factor-quantized","slug":"disentanglement-with-factor-quantized","title":"Disentanglement with Factor Quantized Variational Autoencoders","date":"2024-09-23","arxiv_id":"2409.14851","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-vision-language-survival","slug":"interpretable-vision-language-survival","title":"Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology","date":"2024-09-14","arxiv_id":"2409.09369","repositories_listed":1,"syntology":{"n":16,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":16,"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) · 6 unverified","sample_list":"/paper/interpretable-vision-language-survival#ran","syntology_url":"https://syntology.ai/paper/2409.09369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.09369"}},"official":{"repos":["liupei101/vlsa"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-message-passing-induced-by-energy","slug":"neural-message-passing-induced-by-energy","title":"Neural Message Passing Induced by Energy-Constrained Diffusion","date":"2024-09-13","arxiv_id":"2409.09111","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/neural-message-passing-induced-by-energy#ran","syntology_url":"https://syntology.ai/paper/2409.09111","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.09111"}},"official":null}},{"url":"/paper/redesigning-graph-filter-based-gnns-to-relax","slug":"redesigning-graph-filter-based-gnns-to-relax","title":"Redesigning graph filter-based GNNs to relax the homophily assumption","date":"2024-09-13","arxiv_id":"2409.08676","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-attacks-on-data-attribution","slug":"adversarial-attacks-on-data-attribution","title":"Adversarial Attacks on Data Attribution","date":"2024-09-09","arxiv_id":"2409.05657","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"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) · 0 unverified","sample_list":"/paper/adversarial-attacks-on-data-attribution#ran","syntology_url":"https://syntology.ai/paper/2409.05657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.05657"}},"official":{"repos":["trais-lab/adversarial-attack-data-attribution"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/spatially-aware-diffusion-models-with-cross","slug":"spatially-aware-diffusion-models-with-cross","title":"Spatially-Aware Diffusion Models with Cross-Attention for Global Field Reconstruction with Sparse Observations","date":"2024-08-30","arxiv_id":"2409.00230","repositories_listed":1,"syntology":null},{"url":"/paper/can-transformers-do-enumerative-geometry","slug":"can-transformers-do-enumerative-geometry","title":"Can Transformers Do Enumerative Geometry?","date":"2024-08-27","arxiv_id":"2408.14915","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/can-transformers-do-enumerative-geometry#ran","syntology_url":"https://syntology.ai/paper/2408.14915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.14915"}},"official":{"repos":["Baran-phys/DynamicFormer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-cross-modal-medical-image","slug":"enhancing-cross-modal-medical-image","title":"Enhancing Cross-Modal Medical Image Segmentation through Compositionality","date":"2024-08-21","arxiv_id":"2408.11733","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-composable-representations-for","slug":"unsupervised-composable-representations-for","title":"Unsupervised Composable Representations for Audio","date":"2024-08-19","arxiv_id":"2408.09792","repositories_listed":1,"syntology":null},{"url":"/paper/distinguish-confusion-in-legal-judgment","slug":"distinguish-confusion-in-legal-judgment","title":"Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge","date":"2024-08-18","arxiv_id":"2408.09422","repositories_listed":1,"syntology":null},{"url":"/paper/graph-classification-with-gnns-optimisation","slug":"graph-classification-with-gnns-optimisation","title":"Graph Classification with GNNs: Optimisation, Representation and Inductive Bias","date":"2024-08-17","arxiv_id":"2408.09266","repositories_listed":1,"syntology":null}],"record_sha256":"b8093611c1b3008465cc6d0a6d67c35e4ba67d99a2ab03ea44919e6e137c02a1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}