{"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/ran/1","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":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":3,"rows_per_page":100,"rows":[1,100],"of":284,"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/papers/ran/1","prev":null,"next":"/task/inductive-bias/papers/ran/2","papers":[{"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/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/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/quantum-doubly-stochastic-transformers","slug":"quantum-doubly-stochastic-transformers","title":"Quantum Doubly Stochastic Transformers","date":"2025-04-22","arxiv_id":"2504.16275","repositories_listed":0,"syntology":{"n":4,"n_ran":3,"n_constructed":2,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/quantum-doubly-stochastic-transformers#ran","syntology_url":"https://syntology.ai/paper/2504.16275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.16275"}},"official":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/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/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/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/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/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/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/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/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/context-aware-testing-a-new-paradigm-for","slug":"context-aware-testing-a-new-paradigm-for","title":"Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models","date":"2024-10-31","arxiv_id":"2410.24005","repositories_listed":2,"syntology":{"n":27,"n_ran":22,"n_constructed":0,"n_ran_checked":8,"n_instrument":14,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":6,"n_pointer_only":27,"phrase":"22 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; 14 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/context-aware-testing-a-new-paradigm-for#ran","syntology_url":"https://syntology.ai/paper/2410.24005","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.24005"}},"official":{"repos":["pauliusrauba/SMART_Testing","vanderschaarlab/SMART_Testing"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"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/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/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/failure-proof-non-contrastive-self-supervised","slug":"failure-proof-non-contrastive-self-supervised","title":"Failure-Proof Non-Contrastive Self-Supervised Learning","date":"2024-10-07","arxiv_id":"2410.04959","repositories_listed":0,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/failure-proof-non-contrastive-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2410.04959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04959"}},"official":null}},{"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/longitudinal-segmentation-of-ms-lesions-via","slug":"longitudinal-segmentation-of-ms-lesions-via","title":"Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting","date":"2024-09-20","arxiv_id":"2409.13416","repositories_listed":2,"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/longitudinal-segmentation-of-ms-lesions-via#ran","syntology_url":"https://syntology.ai/paper/2409.13416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.13416"}},"official":{"repos":["MIC-DKFZ/Longitudinal-Difference-Weighting"],"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/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/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/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/depth-wise-convolutions-in-vision","slug":"depth-wise-convolutions-in-vision","title":"Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small Datasets","date":"2024-07-28","arxiv_id":"2407.19394","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":2,"n_no_contract":8,"n_pointer_only":16,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 2 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/depth-wise-convolutions-in-vision#ran","syntology_url":"https://syntology.ai/paper/2407.19394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.19394"}},"official":{"repos":["ztx-100/efficient_vit_with_dw"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/systematic-reasoning-about-relational-domains","slug":"systematic-reasoning-about-relational-domains","title":"Systematic Reasoning About Relational Domains With Graph Neural Networks","date":"2024-07-24","arxiv_id":"2407.17396","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/systematic-reasoning-about-relational-domains#ran","syntology_url":"https://syntology.ai/paper/2407.17396","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.17396"}},"official":{"repos":["erg0dic/gnn-sg"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/partimagenet-dataset-scaling-up-part-based","slug":"partimagenet-dataset-scaling-up-part-based","title":"PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition","date":"2024-07-15","arxiv_id":"2407.10918","repositories_listed":1,"syntology":{"n":19,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/partimagenet-dataset-scaling-up-part-based#ran","syntology_url":"https://syntology.ai/paper/2407.10918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.10918"}},"official":{"repos":["LixiaoTHU/PartImageNetPP"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-attention-with-random-rewiring","slug":"graph-attention-with-random-rewiring","title":"Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention","date":"2024-07-08","arxiv_id":"2407.05649","repositories_listed":0,"syntology":{"n":10,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":10,"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) · 6 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/graph-attention-with-random-rewiring#ran","syntology_url":"https://syntology.ai/paper/2407.05649","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05649"}},"official":null}},{"url":"/paper/lpgd-a-general-framework-for-backpropagation","slug":"lpgd-a-general-framework-for-backpropagation","title":"LPGD: A General Framework for Backpropagation through Embedded Optimization Layers","date":"2024-07-08","arxiv_id":"2407.05920","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lpgd-a-general-framework-for-backpropagation#ran","syntology_url":"https://syntology.ai/paper/2407.05920","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05920"}},"official":{"repos":["martius-lab/diffcp-lpgd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unlocking-continual-learning-abilities-in","slug":"unlocking-continual-learning-abilities-in","title":"Unlocking Continual Learning Abilities in Language Models","date":"2024-06-25","arxiv_id":"2406.17245","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":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) · 2 unverified","sample_list":"/paper/unlocking-continual-learning-abilities-in#ran","syntology_url":"https://syntology.ai/paper/2406.17245","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17245"}},"official":{"repos":["wenyudu/migu"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/probing-the-effects-of-broken-symmetries-in","slug":"probing-the-effects-of-broken-symmetries-in","title":"Probing the effects of broken symmetries in machine learning","date":"2024-06-25","arxiv_id":"2406.17747","repositories_listed":1,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/probing-the-effects-of-broken-symmetries-in#ran","syntology_url":"https://syntology.ai/paper/2406.17747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17747"}},"official":{"repos":["spozdn/pet"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-cross-domain-few-shot","slug":"exploring-cross-domain-few-shot","title":"Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting","date":"2024-06-24","arxiv_id":"2406.16422","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":9,"phrase":"5 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; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/exploring-cross-domain-few-shot#ran","syntology_url":"https://syntology.ai/paper/2406.16422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.16422"}},"official":{"repos":["tinkez/fap_cdfsc"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-exact-computation-of-inductive-bias","slug":"towards-exact-computation-of-inductive-bias","title":"Towards Exact Computation of Inductive Bias","date":"2024-06-22","arxiv_id":"2406.15941","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/towards-exact-computation-of-inductive-bias#ran","syntology_url":"https://syntology.ai/paper/2406.15941","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.15941"}},"official":{"repos":["FieteLab/Exact-Inductive-Bias"],"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/composing-object-relations-and-attributes-for-1","slug":"composing-object-relations-and-attributes-for-1","title":"Composing Object Relations and Attributes for Image-Text Matching","date":"2024-06-17","arxiv_id":"2406.11820","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":1,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/composing-object-relations-and-attributes-for-1#ran","syntology_url":"https://syntology.ai/paper/2406.11820","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11820"}},"official":{"repos":["vkhoi/cora_cvpr24"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unveiling-encoder-free-vision-language-models","slug":"unveiling-encoder-free-vision-language-models","title":"Unveiling Encoder-Free Vision-Language Models","date":"2024-06-17","arxiv_id":"2406.11832","repositories_listed":1,"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/unveiling-encoder-free-vision-language-models#ran","syntology_url":"https://syntology.ai/paper/2406.11832","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11832"}},"official":{"repos":["baaivision/eve"],"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/scale-equivariant-graph-metanetworks","slug":"scale-equivariant-graph-metanetworks","title":"Scale Equivariant Graph Metanetworks","date":"2024-06-15","arxiv_id":"2406.10685","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scale-equivariant-graph-metanetworks#ran","syntology_url":"https://syntology.ai/paper/2406.10685","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.10685"}},"official":{"repos":["jkalogero/scalegmn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/correlation-aware-coarse-to-fine-mlps-for","slug":"correlation-aware-coarse-to-fine-mlps-for","title":"Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration","date":"2024-05-31","arxiv_id":"2406.00123","repositories_listed":1,"syntology":{"n":17,"n_ran":14,"n_constructed":11,"n_ran_checked":12,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":17,"phrase":"14 ran (of which 11 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/correlation-aware-coarse-to-fine-mlps-for#ran","syntology_url":"https://syntology.ai/paper/2406.00123","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.00123"}},"official":{"repos":["mungomeng/registration-corrmlp"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":11,"n_ran_no_instrument_failure":12,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-latent-graph-structures-and-their","slug":"learning-latent-graph-structures-and-their","title":"Learning Latent Graph Structures and their Uncertainty","date":"2024-05-30","arxiv_id":"2405.19933","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-latent-graph-structures-and-their#ran","syntology_url":"https://syntology.ai/paper/2405.19933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.19933"}},"official":{"repos":["allemanenti/Learning-Calibrated-Structures"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sequence-augmented-se-3-flow-matching-for","slug":"sequence-augmented-se-3-flow-matching-for","title":"Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation","date":"2024-05-30","arxiv_id":"2405.20313","repositories_listed":1,"syntology":{"n":20,"n_ran":17,"n_constructed":0,"n_ran_checked":15,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":20,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/sequence-augmented-se-3-flow-matching-for#ran","syntology_url":"https://syntology.ai/paper/2405.20313","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.20313"}},"official":{"repos":["dreamfold/foldflow"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":3,"ran_from_kinds":["community","official"]}}},{"url":"/paper/e-n-equivariant-topological-neural-networks","slug":"e-n-equivariant-topological-neural-networks","title":"E(n) Equivariant Topological Neural Networks","date":"2024-05-24","arxiv_id":"2405.15429","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/e-n-equivariant-topological-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2405.15429","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.15429"}},"official":{"repos":["NSAPH-Projects/topological-equivariant-networks"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mlps-learn-in-context","slug":"mlps-learn-in-context","title":"MLPs Learn In-Context on Regression and Classification Tasks","date":"2024-05-24","arxiv_id":"2405.15618","repositories_listed":2,"syntology":{"n":16,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mlps-learn-in-context#ran","syntology_url":"https://syntology.ai/paper/2405.15618","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.15618"}},"official":{"repos":["wtong98/mlp-icl"],"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":["listed","official"]}}},{"url":"/paper/symmetric-linear-bandits-with-hidden-symmetry","slug":"symmetric-linear-bandits-with-hidden-symmetry","title":"Symmetric Linear Bandits with Hidden Symmetry","date":"2024-05-22","arxiv_id":"2405.13899","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/symmetric-linear-bandits-with-hidden-symmetry#ran","syntology_url":"https://syntology.ai/paper/2405.13899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.13899"}},"official":{"repos":["namtrankekl/symmetric-linear-bandit-with-hidden-symmetry"],"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/binning-as-a-pretext-task-improving-self","slug":"binning-as-a-pretext-task-improving-self","title":"Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains","date":"2024-05-13","arxiv_id":"2405.07414","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":1,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"4 ran (of which 1 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) · 2 unverified","sample_list":"/paper/binning-as-a-pretext-task-improving-self#ran","syntology_url":"https://syntology.ai/paper/2405.07414","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.07414"}},"official":{"repos":["kyungeun-lee/tabularbinning"],"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/sc-otgm-single-cell-perturbation-modeling-by","slug":"sc-otgm-single-cell-perturbation-modeling-by","title":"sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures","date":"2024-05-06","arxiv_id":"2405.03726","repositories_listed":0,"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/sc-otgm-single-cell-perturbation-modeling-by#ran","syntology_url":"https://syntology.ai/paper/2405.03726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.03726"}},"official":null}},{"url":"/paper/u-dits-downsample-tokens-in-u-shaped","slug":"u-dits-downsample-tokens-in-u-shaped","title":"U-DiTs: Downsample Tokens in U-Shaped Diffusion Transformers","date":"2024-05-04","arxiv_id":"2405.02730","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/u-dits-downsample-tokens-in-u-shaped#ran","syntology_url":"https://syntology.ai/paper/2405.02730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.02730"}},"official":{"repos":["yuchuantian/u-dit"],"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/learning-syntax-without-planting-trees","slug":"learning-syntax-without-planting-trees","title":"Learning Syntax Without Planting Trees: Understanding When and Why Transformers Generalize Hierarchically","date":"2024-04-25","arxiv_id":"2404.16367","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/learning-syntax-without-planting-trees#ran","syntology_url":"https://syntology.ai/paper/2404.16367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.16367"}},"official":{"repos":["kabirahuja2431/transformers-hg"],"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/gradformer-graph-transformer-with-exponential","slug":"gradformer-graph-transformer-with-exponential","title":"Gradformer: Graph Transformer with Exponential Decay","date":"2024-04-24","arxiv_id":"2404.15729","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/gradformer-graph-transformer-with-exponential#ran","syntology_url":"https://syntology.ai/paper/2404.15729","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.15729"}},"official":{"repos":["liuchuang0059/gradformer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-causal-nature-of-sentiment-analysis","slug":"on-the-causal-nature-of-sentiment-analysis","title":"Do LLMs Think Fast and Slow? A Causal Study on Sentiment Analysis","date":"2024-04-17","arxiv_id":"2404.11055","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":6,"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/on-the-causal-nature-of-sentiment-analysis#ran","syntology_url":"https://syntology.ai/paper/2404.11055","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.11055"}},"official":{"repos":["cogito233/causal-sa"],"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/learning-to-rank-patches-for-unbiased-image","slug":"learning-to-rank-patches-for-unbiased-image","title":"Learning to Rank Patches for Unbiased Image Redundancy Reduction","date":"2024-03-31","arxiv_id":"2404.00680","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":1,"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 1 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/learning-to-rank-patches-for-unbiased-image#ran","syntology_url":"https://syntology.ai/paper/2404.00680","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00680"}},"official":{"repos":["irslu/ltrp"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/spectral-convolutional-transformer","slug":"spectral-convolutional-transformer","title":"Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis","date":"2024-03-26","arxiv_id":"2403.18063","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/spectral-convolutional-transformer#ran","syntology_url":"https://syntology.ai/paper/2403.18063","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18063"}},"official":{"repos":["badripatro/heracles","badripatro/sct"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/simba-simplified-mamba-based-architecture-for","slug":"simba-simplified-mamba-based-architecture-for","title":"SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series","date":"2024-03-22","arxiv_id":"2403.15360","repositories_listed":3,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/simba-simplified-mamba-based-architecture-for#ran","syntology_url":"https://syntology.ai/paper/2403.15360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.15360"}},"official":{"repos":["badripatro/simba"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-markov-random-field-for-stereo","slug":"neural-markov-random-field-for-stereo","title":"Neural Markov Random Field for Stereo Matching","date":"2024-03-17","arxiv_id":"2403.11193","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"12 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/neural-markov-random-field-for-stereo#ran","syntology_url":"https://syntology.ai/paper/2403.11193","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11193"}},"official":{"repos":["aeolusguan/NMRF"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/xb-maml-learning-expandable-basis-parameters","slug":"xb-maml-learning-expandable-basis-parameters","title":"XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage","date":"2024-03-11","arxiv_id":"2403.06768","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/xb-maml-learning-expandable-basis-parameters#ran","syntology_url":"https://syntology.ai/paper/2403.06768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06768"}},"official":{"repos":["johnjaejunlee95/xb-maml"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-learner-parameterization-by","slug":"few-shot-learner-parameterization-by","title":"Few-shot Learner Parameterization by Diffusion Time-steps","date":"2024-03-05","arxiv_id":"2403.02649","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/few-shot-learner-parameterization-by#ran","syntology_url":"https://syntology.ai/paper/2403.02649","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.02649"}},"official":{"repos":["yue-zhongqi/tif"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/defining-expertise-applications-to-treatment","slug":"defining-expertise-applications-to-treatment","title":"Defining Expertise: Applications to Treatment Effect Estimation","date":"2024-03-01","arxiv_id":"2403.00694","repositories_listed":2,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/defining-expertise-applications-to-treatment#ran","syntology_url":"https://syntology.ai/paper/2403.00694","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.00694"}},"official":{"repos":["qiyaowei/expertise","vanderschaarlab/expertise"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/tokenization-counts-the-impact-of","slug":"tokenization-counts-the-impact-of","title":"Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs","date":"2024-02-22","arxiv_id":"2402.14903","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/tokenization-counts-the-impact-of#ran","syntology_url":"https://syntology.ai/paper/2402.14903","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.14903"}},"official":{"repos":["aadityasingh/tokenizationcounts"],"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/scaling-physics-informed-hard-constraints","slug":"scaling-physics-informed-hard-constraints","title":"Scaling physics-informed hard constraints with mixture-of-experts","date":"2024-02-20","arxiv_id":"2402.13412","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/scaling-physics-informed-hard-constraints#ran","syntology_url":"https://syntology.ai/paper/2402.13412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.13412"}},"official":{"repos":["ask-berkeley/physics-nns-hard-constraints"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/parcv2-physics-aware-recurrent-convolutional","slug":"parcv2-physics-aware-recurrent-convolutional","title":"PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling","date":"2024-02-19","arxiv_id":"2402.12503","repositories_listed":1,"syntology":{"n":6,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"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) · 5 unverified","sample_list":"/paper/parcv2-physics-aware-recurrent-convolutional#ran","syntology_url":"https://syntology.ai/paper/2402.12503","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12503"}},"official":{"repos":["hphong1990/parcv2"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/locality-sensitive-hashing-based-efficient","slug":"locality-sensitive-hashing-based-efficient","title":"Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics","date":"2024-02-19","arxiv_id":"2402.12535","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":1,"n_ran_checked":1,"n_instrument":11,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"12 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; 11 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/locality-sensitive-hashing-based-efficient#ran","syntology_url":"https://syntology.ai/paper/2402.12535","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12535"}},"official":{"repos":["graph-com/hept"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fvit-a-focal-vision-transformer-with-gabor","slug":"fvit-a-focal-vision-transformer-with-gabor","title":"FViT: A Focal Vision Transformer with Gabor Filter","date":"2024-02-17","arxiv_id":"2402.11303","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/fvit-a-focal-vision-transformer-with-gabor#ran","syntology_url":"https://syntology.ai/paper/2402.11303","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11303"}},"official":{"repos":["nkusyl/fvit"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/contiformer-continuous-time-transformer-for-1","slug":"contiformer-continuous-time-transformer-for-1","title":"ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling","date":"2024-02-16","arxiv_id":"2402.10635","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/contiformer-continuous-time-transformer-for-1#ran","syntology_url":"https://syntology.ai/paper/2402.10635","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10635"}},"official":{"repos":["microsoft/SeqML"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/time-series-diffusion-in-the-frequency-domain","slug":"time-series-diffusion-in-the-frequency-domain","title":"Time Series Diffusion in the Frequency Domain","date":"2024-02-08","arxiv_id":"2402.05933","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":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) · 0 unverified","sample_list":"/paper/time-series-diffusion-in-the-frequency-domain#ran","syntology_url":"https://syntology.ai/paper/2402.05933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.05933"}},"official":{"repos":["jonathancrabbe/fourierdiffusion"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-completeness-of-invariant-geometric","slug":"on-the-completeness-of-invariant-geometric","title":"On the Completeness of Invariant Geometric Deep Learning Models","date":"2024-02-07","arxiv_id":"2402.04836","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-the-completeness-of-invariant-geometric#ran","syntology_url":"https://syntology.ai/paper/2402.04836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.04836"}},"official":{"repos":["GraphPKU/GeoNGNN"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/topology-informed-graph-transformer","slug":"topology-informed-graph-transformer","title":"Topology-Informed Graph Transformer","date":"2024-02-03","arxiv_id":"2402.02005","repositories_listed":2,"syntology":{"n":21,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":2,"n_no_contract":15,"n_pointer_only":21,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 2 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/topology-informed-graph-transformer#ran","syntology_url":"https://syntology.ai/paper/2402.02005","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02005"}},"official":{"repos":["leemingo/tigt"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/exploring-diffusion-time-steps-for","slug":"exploring-diffusion-time-steps-for","title":"Exploring Diffusion Time-steps for Unsupervised Representation Learning","date":"2024-01-21","arxiv_id":"2401.11430","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exploring-diffusion-time-steps-for#ran","syntology_url":"https://syntology.ai/paper/2401.11430","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.11430"}},"official":{"repos":["yue-zhongqi/diti"],"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/imputeformer-graph-transformers-for","slug":"imputeformer-graph-transformers-for","title":"ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation","date":"2023-12-04","arxiv_id":"2312.01728","repositories_listed":2,"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/imputeformer-graph-transformers-for#ran","syntology_url":"https://syntology.ai/paper/2312.01728","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01728"}},"official":{"repos":["WenjieDu/PyPOTS"],"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":["named_in_paper"]}}},{"url":"/paper/energy-based-potential-games-for-joint-motion","slug":"energy-based-potential-games-for-joint-motion","title":"Energy-based Potential Games for Joint Motion Forecasting and Control","date":"2023-12-04","arxiv_id":"2312.01811","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/energy-based-potential-games-for-joint-motion#ran","syntology_url":"https://syntology.ai/paper/2312.01811","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01811"}},"official":{"repos":["rst-tu-dortmund/diff_epo_planner"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/recurrent-distance-encoding-neural-networks","slug":"recurrent-distance-encoding-neural-networks","title":"Recurrent Distance Filtering for Graph Representation Learning","date":"2023-12-03","arxiv_id":"2312.01538","repositories_listed":1,"syntology":{"n":21,"n_ran":12,"n_constructed":2,"n_ran_checked":9,"n_instrument":3,"n_unverified":9,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":21,"phrase":"12 ran (of which 2 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/recurrent-distance-encoding-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2312.01538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01538"}},"official":{"repos":["skeletondyh/gred"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":2,"n_ran_no_instrument_failure":9,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/reward-retinal-waves-for-pre-training","slug":"reward-retinal-waves-for-pre-training","title":"ReWaRD: Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development","date":"2023-11-28","arxiv_id":"2311.17232","repositories_listed":1,"syntology":{"n":17,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":17,"phrase":"11 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; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/reward-retinal-waves-for-pre-training#ran","syntology_url":"https://syntology.ai/paper/2311.17232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17232"}},"official":{"repos":["bennyca/reward"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-and-robust-jet-tagging-at-the-lhc","slug":"efficient-and-robust-jet-tagging-at-the-lhc","title":"Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation","date":"2023-11-23","arxiv_id":"2311.14160","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/efficient-and-robust-jet-tagging-at-the-lhc#ran","syntology_url":"https://syntology.ai/paper/2311.14160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14160"}},"official":{"repos":["ryanliu30/kd4jets"],"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/architecture-matters-uncovering-implicit-1","slug":"architecture-matters-uncovering-implicit-1","title":"Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning","date":"2023-11-05","arxiv_id":"2311.02687","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":0,"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/architecture-matters-uncovering-implicit-1#ran","syntology_url":"https://syntology.ai/paper/2311.02687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.02687"}},"official":{"repos":["pku-ml/architecturemattersgcl"],"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/hyperbolic-graph-neural-networks-at-scale-a","slug":"hyperbolic-graph-neural-networks-at-scale-a","title":"Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach","date":"2023-10-29","arxiv_id":"2310.18918","repositories_listed":0,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/hyperbolic-graph-neural-networks-at-scale-a#ran","syntology_url":"https://syntology.ai/paper/2310.18918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18918"}},"official":null}},{"url":"/paper/c-disentanglement-discovering-causally","slug":"c-disentanglement-discovering-causally","title":"C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder","date":"2023-10-26","arxiv_id":"2310.17325","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":3,"n_ran_checked":3,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":8,"phrase":"6 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/c-disentanglement-discovering-causally#ran","syntology_url":"https://syntology.ai/paper/2310.17325","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.17325"}},"official":{"repos":["xliu1231/causal_disentangle"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dual-cognitive-architecture-incorporating","slug":"dual-cognitive-architecture-incorporating","title":"Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning","date":"2023-10-17","arxiv_id":"2310.11341","repositories_listed":2,"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/dual-cognitive-architecture-incorporating#ran","syntology_url":"https://syntology.ai/paper/2310.11341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.11341"}},"official":{"repos":["neurai-lab/dn4il-dataset","neurai-lab/duca"],"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/generative-entropic-neural-optimal-transport","slug":"generative-entropic-neural-optimal-transport","title":"GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics","date":"2023-10-13","arxiv_id":"2310.09254","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generative-entropic-neural-optimal-transport#ran","syntology_url":"https://syntology.ai/paper/2310.09254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09254"}},"official":{"repos":["mucdk/genot"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/how-graph-neural-networks-learn-lessons-from","slug":"how-graph-neural-networks-learn-lessons-from","title":"How Graph Neural Networks Learn: Lessons from Training Dynamics","date":"2023-10-08","arxiv_id":"2310.05105","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/how-graph-neural-networks-learn-lessons-from#ran","syntology_url":"https://syntology.ai/paper/2310.05105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.05105"}},"official":{"repos":["chr26195/residualpropagation"],"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/xval-a-continuous-number-encoding-for-large","slug":"xval-a-continuous-number-encoding-for-large","title":"xVal: A Continuous Numerical Tokenization for Scientific Language Models","date":"2023-10-04","arxiv_id":"2310.02989","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/xval-a-continuous-number-encoding-for-large#ran","syntology_url":"https://syntology.ai/paper/2310.02989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02989"}},"official":{"repos":["PolymathicAI/xVal"],"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/implicit-regularization-of-multi-task","slug":"implicit-regularization-of-multi-task","title":"Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse","date":"2023-10-03","arxiv_id":"2310.02396","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/implicit-regularization-of-multi-task#ran","syntology_url":"https://syntology.ai/paper/2310.02396","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02396"}},"official":{"repos":["sflippl/multi-task"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/convolution-and-attention-mixer-for-synthetic","slug":"convolution-and-attention-mixer-for-synthetic","title":"Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection","date":"2023-09-21","arxiv_id":"2309.12010","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/convolution-and-attention-mixer-for-synthetic#ran","syntology_url":"https://syntology.ai/paper/2309.12010","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.12010"}},"official":{"repos":["summitgao/camixer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-cognitively-inspired-neural-architecture","slug":"a-cognitively-inspired-neural-architecture","title":"A Cognitively-Inspired Neural Architecture for Visual Abstract Reasoning Using Contrastive Perceptual and Conceptual Processing","date":"2023-09-19","arxiv_id":"2309.10532","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-cognitively-inspired-neural-architecture#ran","syntology_url":"https://syntology.ai/paper/2309.10532","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.10532"}},"official":{"repos":["Yang-Yuan/CPCNet"],"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/quantifying-degeneracy-in-singular-models-via","slug":"quantifying-degeneracy-in-singular-models-via","title":"The Local Learning Coefficient: A Singularity-Aware Complexity Measure","date":"2023-08-23","arxiv_id":"2308.12108","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"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 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/quantifying-degeneracy-in-singular-models-via#ran","syntology_url":"https://syntology.ai/paper/2308.12108","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12108"}},"official":{"repos":["edmundlth/scalable_learning_coefficient_with_sgld"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/forecast-mae-self-supervised-pre-training-for","slug":"forecast-mae-self-supervised-pre-training-for","title":"Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders","date":"2023-08-19","arxiv_id":"2308.09882","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/forecast-mae-self-supervised-pre-training-for#ran","syntology_url":"https://syntology.ai/paper/2308.09882","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09882"}},"official":{"repos":["jchengai/forecast-mae"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/investigating-the-interplay-between-features","slug":"investigating-the-interplay-between-features","title":"Investigating the Interplay between Features and Structures in Graph Learning","date":"2023-08-18","arxiv_id":"2308.09570","repositories_listed":1,"syntology":{"n":17,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":17,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/investigating-the-interplay-between-features#ran","syntology_url":"https://syntology.ai/paper/2308.09570","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09570"}},"official":{"repos":["danielecastellana22/feature-structure-interplay-graph-learning"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/cmunext-an-efficient-medical-image","slug":"cmunext-an-efficient-medical-image","title":"CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion","date":"2023-08-02","arxiv_id":"2308.01239","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":0,"n_instrument":6,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 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; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cmunext-an-efficient-medical-image#ran","syntology_url":"https://syntology.ai/paper/2308.01239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01239"}},"official":{"repos":["fenghetan9/cmunext","FengheTan9/Medical-Image-Segmentation-Benchmarks"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/optimizing-patchcore-for-few-many-shot","slug":"optimizing-patchcore-for-few-many-shot","title":"Optimizing PatchCore for Few/many-shot Anomaly Detection","date":"2023-07-20","arxiv_id":"2307.10792","repositories_listed":3,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":14,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/optimizing-patchcore-for-few-many-shot#ran","syntology_url":"https://syntology.ai/paper/2307.10792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.10792"}},"official":{"repos":["scortexio/patchcore-few-shot"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/expressive-monotonic-neural-networks","slug":"expressive-monotonic-neural-networks","title":"Expressive Monotonic Neural Networks","date":"2023-07-14","arxiv_id":"2307.07512","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/expressive-monotonic-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2307.07512","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.07512"}},"official":{"repos":["niklasnolte/hlt_2track"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/end-to-end-supervised-multilabel-contrastive","slug":"end-to-end-supervised-multilabel-contrastive","title":"End-to-End Supervised Multilabel Contrastive Learning","date":"2023-07-08","arxiv_id":"2307.03967","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/end-to-end-supervised-multilabel-contrastive#ran","syntology_url":"https://syntology.ai/paper/2307.03967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.03967"}},"official":{"repos":["mahdihosseini/kmcl"],"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/re-think-and-re-design-graph-neural-networks","slug":"re-think-and-re-design-graph-neural-networks","title":"Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals","date":"2023-07-01","arxiv_id":"2307.00222","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/re-think-and-re-design-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2307.00222","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.00222"}},"official":{"repos":["Dandy5721/GNN-PDE-COV"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/protogate-prototype-based-neural-networks","slug":"protogate-prototype-based-neural-networks","title":"ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data","date":"2023-06-21","arxiv_id":"2306.12330","repositories_listed":1,"syntology":{"n":14,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/protogate-prototype-based-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2306.12330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.12330"}},"official":{"repos":["SilenceX12138/ProtoGate"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/hidden-biases-of-end-to-end-driving-models","slug":"hidden-biases-of-end-to-end-driving-models","title":"Hidden Biases of End-to-End Driving Models","date":"2023-06-13","arxiv_id":"2306.07957","repositories_listed":1,"syntology":{"n":17,"n_ran":13,"n_constructed":10,"n_ran_checked":10,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"13 ran (of which 10 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/hidden-biases-of-end-to-end-driving-models#ran","syntology_url":"https://syntology.ai/paper/2306.07957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07957"}},"official":{"repos":["autonomousvision/carla_garage"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":10,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-efficacy-of-3d-point-cloud","slug":"on-the-efficacy-of-3d-point-cloud","title":"On the Efficacy of 3D Point Cloud Reinforcement Learning","date":"2023-06-11","arxiv_id":"2306.06799","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/on-the-efficacy-of-3d-point-cloud#ran","syntology_url":"https://syntology.ai/paper/2306.06799","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.06799"}},"official":{"repos":["lz1oceani/pointcloud_rl"],"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/on-robot-grasp-learning-using-equivariant","slug":"on-robot-grasp-learning-using-equivariant","title":"On Robot Grasp Learning Using Equivariant Models","date":"2023-06-10","arxiv_id":"2306.06489","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-robot-grasp-learning-using-equivariant#ran","syntology_url":"https://syntology.ai/paper/2306.06489","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.06489"}},"official":{"repos":["zxp-s-works/se2-equivariant-grasp-learning"],"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/in-context-learning-through-the-bayesian","slug":"in-context-learning-through-the-bayesian","title":"In-Context Learning through the Bayesian Prism","date":"2023-06-08","arxiv_id":"2306.04891","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/in-context-learning-through-the-bayesian#ran","syntology_url":"https://syntology.ai/paper/2306.04891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.04891"}},"official":{"repos":["mdrpanwar/icl-bayesian-prism"],"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/dyffusion-a-dynamics-informed-diffusion-model-1","slug":"dyffusion-a-dynamics-informed-diffusion-model-1","title":"DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting","date":"2023-06-03","arxiv_id":"2306.01984","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dyffusion-a-dynamics-informed-diffusion-model-1#ran","syntology_url":"https://syntology.ai/paper/2306.01984","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.01984"}},"official":{"repos":["rose-stl-lab/dyffusion"],"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/vocos-closing-the-gap-between-time-domain-and","slug":"vocos-closing-the-gap-between-time-domain-and","title":"Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis","date":"2023-06-01","arxiv_id":"2306.00814","repositories_listed":4,"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":1,"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/vocos-closing-the-gap-between-time-domain-and#ran","syntology_url":"https://syntology.ai/paper/2306.00814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00814"}},"official":{"repos":["gemelo-ai/vocos"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/lottery-tickets-in-evolutionary-optimization","slug":"lottery-tickets-in-evolutionary-optimization","title":"Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability","date":"2023-05-31","arxiv_id":"2306.00045","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lottery-tickets-in-evolutionary-optimization#ran","syntology_url":"https://syntology.ai/paper/2306.00045","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00045"}},"official":{"repos":["roberttlange/es-lottery"],"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/disentanglement-via-latent-quantization-1","slug":"disentanglement-via-latent-quantization-1","title":"Disentanglement via Latent Quantization","date":"2023-05-28","arxiv_id":"2305.18378","repositories_listed":1,"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":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) · 2 unverified","sample_list":"/paper/disentanglement-via-latent-quantization-1#ran","syntology_url":"https://syntology.ai/paper/2305.18378","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18378"}},"official":{"repos":["kylehkhsu/latent_quantization"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}}],"record_sha256":"6f303c620d07f86cc7d45db2d19df31da350c5fd62835cce93a3eac8254d4580","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}