{"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/computational-efficiency/papers/ran/3","list_of":"/task/computational-efficiency","task":"Computational Efficiency","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":3,"pages_in_order":4,"rows_per_page":100,"rows":[201,300],"of":369,"counts":{"archive_papers_tagged":4891,"with_a_code_link":1644,"where_syntology_ran_a_sample":369,"not_listed_spam_title":0,"listed":4891,"listed_where_code_ran":369,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":307,"every_run_a_failure_of_syntologys_instrument":62,"listed_with_a_run_with_no_instrument_failure":307,"listed_every_run_a_failure_of_syntologys_instrument":62,"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/computational-efficiency/papers/ran/1","prev":"/task/computational-efficiency/papers/ran/2","next":"/task/computational-efficiency/papers/ran/4","papers":[{"url":"/paper/mgdepth-motion-guided-cost-volume-for-self","slug":"mgdepth-motion-guided-cost-volume-for-self","title":"Manydepth2: Motion-Aware Self-Supervised Multi-Frame Monocular Depth Estimation in Dynamic Scenes","date":"2023-12-23","arxiv_id":"2312.15268","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":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mgdepth-motion-guided-cost-volume-for-self#ran","syntology_url":"https://syntology.ai/paper/2312.15268","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.15268"}},"official":null}},{"url":"/paper/zeroshape-regression-based-zero-shot-shape","slug":"zeroshape-regression-based-zero-shot-shape","title":"ZeroShape: Regression-based Zero-shot Shape Reconstruction","date":"2023-12-21","arxiv_id":"2312.14198","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/zeroshape-regression-based-zero-shot-shape#ran","syntology_url":"https://syntology.ai/paper/2312.14198","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14198"}},"official":{"repos":["zxhuang1698/ZeroShape"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-decision-boundary-based-out-of","slug":"fast-decision-boundary-based-out-of","title":"Fast Decision Boundary based Out-of-Distribution Detector","date":"2023-12-15","arxiv_id":"2312.11536","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fast-decision-boundary-based-out-of#ran","syntology_url":"https://syntology.ai/paper/2312.11536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.11536"}},"official":{"repos":["litianliu/fdbd-ood"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/agent-attention-on-the-integration-of-softmax","slug":"agent-attention-on-the-integration-of-softmax","title":"Agent Attention: On the Integration of Softmax and Linear Attention","date":"2023-12-14","arxiv_id":"2312.08874","repositories_listed":2,"syntology":{"n":19,"n_ran":13,"n_constructed":0,"n_ran_checked":9,"n_instrument":4,"n_unverified":6,"n_honours":1,"n_violates":1,"n_no_contract":7,"n_pointer_only":19,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 1 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/agent-attention-on-the-integration-of-softmax#ran","syntology_url":"https://syntology.ai/paper/2312.08874","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08874"}},"official":{"repos":["leaplabthu/agent-attention"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/videolcm-video-latent-consistency-model","slug":"videolcm-video-latent-consistency-model","title":"VideoLCM: Video Latent Consistency Model","date":"2023-12-14","arxiv_id":"2312.09109","repositories_listed":2,"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/videolcm-video-latent-consistency-model#ran","syntology_url":"https://syntology.ai/paper/2312.09109","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09109"}},"official":null}},{"url":"/paper/mamba-linear-time-sequence-modeling-with","slug":"mamba-linear-time-sequence-modeling-with","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","date":"2023-12-01","arxiv_id":"2312.00752","repositories_listed":35,"syntology":{"n":62,"n_ran":28,"n_constructed":7,"n_ran_checked":21,"n_instrument":7,"n_unverified":34,"n_honours":0,"n_violates":0,"n_no_contract":21,"n_pointer_only":29,"phrase":"28 ran (of which 7 constructed an object rather than computing a result; 21 with no instrument failure: 0 honoured, 0 violated, 21 with no contract checked; 7 where Syntology's instrument failed) · 34 unverified","sample_list":"/paper/mamba-linear-time-sequence-modeling-with#ran","syntology_url":"https://syntology.ai/paper/2312.00752","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.00752"}},"official":{"repos":["state-spaces/mamba","radarFudan/mamba"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/reds-resource-efficient-deep-subnetworks-for","slug":"reds-resource-efficient-deep-subnetworks-for","title":"REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints","date":"2023-11-22","arxiv_id":"2311.13349","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/reds-resource-efficient-deep-subnetworks-for#ran","syntology_url":"https://syntology.ai/paper/2311.13349","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.13349"}},"official":{"repos":["FraCorti/Deep_Subnetworks_for_Dynamic_Resource_Constraints"],"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/optimization-free-test-time-adaptation-for","slug":"optimization-free-test-time-adaptation-for","title":"Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition","date":"2023-10-28","arxiv_id":"2310.18562","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/optimization-free-test-time-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/2310.18562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18562"}},"official":{"repos":["Claydon-Wang/OFTTA"],"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","unlocated"]}}},{"url":"/paper/accomontage-3-full-band-accompaniment","slug":"accomontage-3-full-band-accompaniment","title":"Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling","date":"2023-10-25","arxiv_id":"2310.16334","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"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) · 3 unverified","sample_list":"/paper/accomontage-3-full-band-accompaniment#ran","syntology_url":"https://syntology.ai/paper/2310.16334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16334"}},"official":{"repos":["zhaojw1998/accomontage-3"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/adam-through-a-second-order-lens","slug":"adam-through-a-second-order-lens","title":"Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens","date":"2023-10-23","arxiv_id":"2310.14963","repositories_listed":1,"syntology":{"n":16,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":16,"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) · 9 unverified","sample_list":"/paper/adam-through-a-second-order-lens#ran","syntology_url":"https://syntology.ai/paper/2310.14963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.14963"}},"official":{"repos":["rmclarke/adamthroughasecondorderlens"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/merging-experts-into-one-improving","slug":"merging-experts-into-one-improving","title":"Merging Experts into One: Improving Computational Efficiency of Mixture of Experts","date":"2023-10-15","arxiv_id":"2310.09832","repositories_listed":1,"syntology":{"n":21,"n_ran":18,"n_constructed":2,"n_ran_checked":15,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":21,"phrase":"18 ran (of which 2 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/merging-experts-into-one-improving#ran","syntology_url":"https://syntology.ai/paper/2310.09832","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09832"}},"official":{"repos":["shwai-he/meo"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":2,"n_ran_no_instrument_failure":15,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-end-to-end-4-bit-inference-on","slug":"towards-end-to-end-4-bit-inference-on","title":"QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models","date":"2023-10-13","arxiv_id":"2310.09259","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/towards-end-to-end-4-bit-inference-on#ran","syntology_url":"https://syntology.ai/paper/2310.09259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09259"}},"official":{"repos":["ist-daslab/quik"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-expressive-se-n-equivariant-networks","slug":"fast-expressive-se-n-equivariant-networks","title":"Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space","date":"2023-10-04","arxiv_id":"2310.02970","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/fast-expressive-se-n-equivariant-networks#ran","syntology_url":"https://syntology.ai/paper/2310.02970","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02970"}},"official":{"repos":["ebekkers/ponita"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"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/deepzero-scaling-up-zeroth-order-optimization","slug":"deepzero-scaling-up-zeroth-order-optimization","title":"DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training","date":"2023-10-03","arxiv_id":"2310.02025","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/deepzero-scaling-up-zeroth-order-optimization#ran","syntology_url":"https://syntology.ai/paper/2310.02025","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02025"}},"official":{"repos":["OPTML-Group/DeepZero"],"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/venom-a-vectorized-n-m-format-for-unleashing","slug":"venom-a-vectorized-n-m-format-for-unleashing","title":"VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores","date":"2023-10-03","arxiv_id":"2310.02065","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/venom-a-vectorized-n-m-format-for-unleashing#ran","syntology_url":"https://syntology.ai/paper/2310.02065","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02065"}},"official":{"repos":["udc-gac/venom"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-solver-framework-for-dynamic","slug":"adaptive-solver-framework-for-dynamic","title":"Adaptive-Solver Framework for Dynamic Strategy Selection in Large Language Model Reasoning","date":"2023-10-01","arxiv_id":"2310.01446","repositories_listed":1,"syntology":{"n":11,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":11,"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) · 7 unverified","sample_list":"/paper/adaptive-solver-framework-for-dynamic#ran","syntology_url":"https://syntology.ai/paper/2310.01446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01446"}},"official":{"repos":["john1226966735/adaptive-solver"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/tora-a-tool-integrated-reasoning-agent-for","slug":"tora-a-tool-integrated-reasoning-agent-for","title":"ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving","date":"2023-09-29","arxiv_id":"2309.17452","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":9,"phrase":"11 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tora-a-tool-integrated-reasoning-agent-for#ran","syntology_url":"https://syntology.ai/paper/2309.17452","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.17452"}},"official":{"repos":["microsoft/tora"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/advanced-volleyball-stats-for-all-levels","slug":"advanced-volleyball-stats-for-all-levels","title":"Advanced Volleyball Stats for All Levels: Automatic Setting Tactic Detection and Classification with a Single Camera","date":"2023-09-26","arxiv_id":"2309.14753","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":5,"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/advanced-volleyball-stats-for-all-levels#ran","syntology_url":"https://syntology.ai/paper/2309.14753","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14753"}},"official":{"repos":["volleyIEEE/VolleyStats"],"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/evidential-deep-learning-enhancing-predictive","slug":"evidential-deep-learning-enhancing-predictive","title":"Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications","date":"2023-09-22","arxiv_id":"2309.13207","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":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/evidential-deep-learning-enhancing-predictive#ran","syntology_url":"https://syntology.ai/paper/2309.13207","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13207"}},"official":{"repos":["AI2ES/miles-guess"],"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/adapt-and-diffuse-sample-adaptive","slug":"adapt-and-diffuse-sample-adaptive","title":"Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models","date":"2023-09-12","arxiv_id":"2309.06642","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":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/adapt-and-diffuse-sample-adaptive#ran","syntology_url":"https://syntology.ai/paper/2309.06642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.06642"}},"official":{"repos":["z-fabian/flash-diffusion"],"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/on-large-language-models-selection-bias-in","slug":"on-large-language-models-selection-bias-in","title":"Large Language Models Are Not Robust Multiple Choice Selectors","date":"2023-09-07","arxiv_id":"2309.03882","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":6,"n_pointer_only":12,"phrase":"11 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-large-language-models-selection-bias-in#ran","syntology_url":"https://syntology.ai/paper/2309.03882","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.03882"}},"official":{"repos":["chujiezheng/llm-mcq-bias"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/bayotide-bayesian-online-multivariate-time","slug":"bayotide-bayesian-online-multivariate-time","title":"BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition","date":"2023-08-28","arxiv_id":"2308.14906","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":1,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":4,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 5 with no instrument failure: 4 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/bayotide-bayesian-online-multivariate-time#ran","syntology_url":"https://syntology.ai/paper/2308.14906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14906"}},"official":{"repos":["xuangu-fang/bayotide"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/omniquant-omnidirectionally-calibrated","slug":"omniquant-omnidirectionally-calibrated","title":"OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models","date":"2023-08-25","arxiv_id":"2308.13137","repositories_listed":2,"syntology":{"n":16,"n_ran":10,"n_constructed":1,"n_ran_checked":8,"n_instrument":2,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":9,"phrase":"10 ran (of which 1 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) · 6 unverified","sample_list":"/paper/omniquant-omnidirectionally-calibrated#ran","syntology_url":"https://syntology.ai/paper/2308.13137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13137"}},"official":{"repos":["opengvlab/omniquant"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/scqpth-an-efficient-differentiable-splitting","slug":"scqpth-an-efficient-differentiable-splitting","title":"SCQPTH: an efficient differentiable splitting method for convex quadratic programming","date":"2023-08-16","arxiv_id":"2308.08232","repositories_listed":2,"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":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/scqpth-an-efficient-differentiable-splitting#ran","syntology_url":"https://syntology.ai/paper/2308.08232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.08232"}},"official":{"repos":["ipo-lab/scqpth","ipo-lab/scqpth_bench"],"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/petformer-long-term-time-series-forecasting","slug":"petformer-long-term-time-series-forecasting","title":"PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer","date":"2023-08-09","arxiv_id":"2308.04791","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/petformer-long-term-time-series-forecasting#ran","syntology_url":"https://syntology.ai/paper/2308.04791","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.04791"}},"official":{"repos":["ACAT-SCUT/PETformer"],"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/a-private-watermark-for-large-language-models","slug":"a-private-watermark-for-large-language-models","title":"An Unforgeable Publicly Verifiable Watermark for Large Language Models","date":"2023-07-30","arxiv_id":"2307.16230","repositories_listed":3,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":1,"n_no_contract":1,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 1 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-private-watermark-for-large-language-models#ran","syntology_url":"https://syntology.ai/paper/2307.16230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.16230"}},"official":{"repos":["THU-BPM/private_watermark","thu-bpm/markllm","thu-bpm/unforgeable_watermark"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/from-continuous-time-formulations-to","slug":"from-continuous-time-formulations-to","title":"From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs","date":"2023-07-28","arxiv_id":"2307.15496","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/from-continuous-time-formulations-to#ran","syntology_url":"https://syntology.ai/paper/2307.15496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.15496"}},"official":{"repos":["lorenzrichter/PDE-backward-solver"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/flow-matching-in-latent-space","slug":"flow-matching-in-latent-space","title":"Flow Matching in Latent Space","date":"2023-07-17","arxiv_id":"2307.08698","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/flow-matching-in-latent-space#ran","syntology_url":"https://syntology.ai/paper/2307.08698","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08698"}},"official":{"repos":["vinairesearch/lfm"],"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/fdapt-federated-domain-adaptive-pre-training","slug":"fdapt-federated-domain-adaptive-pre-training","title":"FDAPT: Federated Domain-adaptive Pre-training for Language Models","date":"2023-07-12","arxiv_id":"2307.06933","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"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) · 2 unverified","sample_list":"/paper/fdapt-federated-domain-adaptive-pre-training#ran","syntology_url":"https://syntology.ai/paper/2307.06933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.06933"}},"official":{"repos":["scylj1/FDAPT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/metropolis-sampling-for-constrained-diffusion","slug":"metropolis-sampling-for-constrained-diffusion","title":"Metropolis Sampling for Constrained Diffusion Models","date":"2023-07-11","arxiv_id":"2307.05439","repositories_listed":0,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/metropolis-sampling-for-constrained-diffusion#ran","syntology_url":"https://syntology.ai/paper/2307.05439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.05439"}},"official":null}},{"url":"/paper/deep-speech-synthesis-from-mri-based","slug":"deep-speech-synthesis-from-mri-based","title":"Deep Speech Synthesis from MRI-Based Articulatory Representations","date":"2023-07-05","arxiv_id":"2307.02471","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/deep-speech-synthesis-from-mri-based#ran","syntology_url":"https://syntology.ai/paper/2307.02471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.02471"}},"official":{"repos":["articulatory/articulatory"],"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/practical-and-asymptotically-exact-1","slug":"practical-and-asymptotically-exact-1","title":"Practical and Asymptotically Exact Conditional Sampling in Diffusion Models","date":"2023-06-30","arxiv_id":"2306.17775","repositories_listed":2,"syntology":{"n":15,"n_ran":9,"n_constructed":1,"n_ran_checked":2,"n_instrument":7,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":15,"phrase":"9 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 7 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/practical-and-asymptotically-exact-1#ran","syntology_url":"https://syntology.ai/paper/2306.17775","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.17775"}},"official":{"repos":["blt2114/twisted_diffusion_sampler"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/rl4co-an-extensive-reinforcement-learning-for","slug":"rl4co-an-extensive-reinforcement-learning-for","title":"RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark","date":"2023-06-29","arxiv_id":"2306.17100","repositories_listed":3,"syntology":{"n":16,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":3,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/rl4co-an-extensive-reinforcement-learning-for#ran","syntology_url":"https://syntology.ai/paper/2306.17100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.17100"}},"official":{"repos":["ai4co/rl4co","pytorch/rl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/dnabert-2-efficient-foundation-model-and","slug":"dnabert-2-efficient-foundation-model-and","title":"DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome","date":"2023-06-26","arxiv_id":"2306.15006","repositories_listed":6,"syntology":{"n":23,"n_ran":13,"n_constructed":0,"n_ran_checked":9,"n_instrument":4,"n_unverified":10,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/dnabert-2-efficient-foundation-model-and#ran","syntology_url":"https://syntology.ai/paper/2306.15006","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.15006"}},"official":{"repos":["magics-lab/dnabert_2","zhihan1996/dnabert_2"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/efficiently-learning-the-graph-for-semi","slug":"efficiently-learning-the-graph-for-semi","title":"Efficiently Learning the Graph for Semi-supervised Learning","date":"2023-06-12","arxiv_id":"2306.07098","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/efficiently-learning-the-graph-for-semi#ran","syntology_url":"https://syntology.ai/paper/2306.07098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07098"}},"official":{"repos":["maxwelljones14/efficient-ssl"],"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/unbalanced-optimal-transport-meets-sliced","slug":"unbalanced-optimal-transport-meets-sliced","title":"Slicing Unbalanced Optimal Transport","date":"2023-06-12","arxiv_id":"2306.07176","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/unbalanced-optimal-transport-meets-sliced#ran","syntology_url":"https://syntology.ai/paper/2306.07176","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07176"}},"official":{"repos":["clbonet/Slicing_Unbalanced_Optimal_Transport"],"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/multi-body-se-3-equivariance-for-unsupervised","slug":"multi-body-se-3-equivariance-for-unsupervised","title":"Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation","date":"2023-06-08","arxiv_id":"2306.05584","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/multi-body-se-3-equivariance-for-unsupervised#ran","syntology_url":"https://syntology.ai/paper/2306.05584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05584"}},"official":{"repos":["jx-zhong-for-academic-purpose/Multibody_SE3"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/famo-fast-adaptive-multitask-optimization-1","slug":"famo-fast-adaptive-multitask-optimization-1","title":"FAMO: Fast Adaptive Multitask Optimization","date":"2023-06-06","arxiv_id":"2306.03792","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/famo-fast-adaptive-multitask-optimization-1#ran","syntology_url":"https://syntology.ai/paper/2306.03792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03792"}},"official":{"repos":["cranial-xix/famo"],"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/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/torchrl-a-data-driven-decision-making-library","slug":"torchrl-a-data-driven-decision-making-library","title":"TorchRL: A data-driven decision-making library for PyTorch","date":"2023-06-01","arxiv_id":"2306.00577","repositories_listed":2,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":9,"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) · 1 unverified","sample_list":"/paper/torchrl-a-data-driven-decision-making-library#ran","syntology_url":"https://syntology.ai/paper/2306.00577","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00577"}},"official":null}},{"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/confronting-ambiguity-in-6d-object-pose","slug":"confronting-ambiguity-in-6d-object-pose","title":"Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)","date":"2023-05-25","arxiv_id":"2305.15873","repositories_listed":1,"syntology":{"n":23,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":0,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/confronting-ambiguity-in-6d-object-pose#ran","syntology_url":"https://syntology.ai/paper/2305.15873","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15873"}},"official":{"repos":["Ending2015a/liepose-diffusion"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-incomplete-factorization-learning","slug":"neural-incomplete-factorization-learning","title":"Neural incomplete factorization: learning preconditioners for the conjugate gradient method","date":"2023-05-25","arxiv_id":"2305.16368","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":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-incomplete-factorization-learning#ran","syntology_url":"https://syntology.ai/paper/2305.16368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.16368"}},"official":{"repos":["paulhausner/neural-incomplete-factorization"],"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/rwkv-reinventing-rnns-for-the-transformer-era","slug":"rwkv-reinventing-rnns-for-the-transformer-era","title":"RWKV: Reinventing RNNs for the Transformer Era","date":"2023-05-22","arxiv_id":"2305.13048","repositories_listed":14,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/rwkv-reinventing-rnns-for-the-transformer-era#ran","syntology_url":"https://syntology.ai/paper/2305.13048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13048"}},"official":{"repos":["BlinkDL/RWKV-LM","blinkdl/chatrwkv"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/tune-mode-convbn-blocks-for-efficient","slug":"tune-mode-convbn-blocks-for-efficient","title":"Efficient ConvBN Blocks for Transfer Learning and Beyond","date":"2023-05-19","arxiv_id":"2305.11624","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tune-mode-convbn-blocks-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2305.11624","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11624"}},"official":{"repos":["apple/ml-tune-mode-convbn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-better-graph-representation-learning","slug":"towards-better-graph-representation-learning","title":"Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering","date":"2023-05-10","arxiv_id":"2305.06102","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-better-graph-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2305.06102","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06102"}},"official":{"repos":["qslim/pdf"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/two-birds-one-stone-a-unified-framework-for","slug":"two-birds-one-stone-a-unified-framework-for","title":"Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers","date":"2023-04-22","arxiv_id":"2304.11335","repositories_listed":1,"syntology":{"n":12,"n_ran":4,"n_constructed":3,"n_ran_checked":4,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":12,"phrase":"4 ran (of which 3 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) · 8 unverified","sample_list":"/paper/two-birds-one-stone-a-unified-framework-for#ran","syntology_url":"https://syntology.ai/paper/2304.11335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.11335"}},"official":{"repos":["NevSNev/UniST"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamicdet-a-unified-dynamic-architecture-for","slug":"dynamicdet-a-unified-dynamic-architecture-for","title":"DynamicDet: A Unified Dynamic Architecture for Object Detection","date":"2023-04-12","arxiv_id":"2304.05552","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dynamicdet-a-unified-dynamic-architecture-for#ran","syntology_url":"https://syntology.ai/paper/2304.05552","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.05552"}},"official":{"repos":["VDIGPKU/DynamicDet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-multiplane-neural-radiance-for-3d","slug":"generative-multiplane-neural-radiance-for-3d","title":"Generative Multiplane Neural Radiance for 3D-Aware Image Generation","date":"2023-04-03","arxiv_id":"2304.01172","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/generative-multiplane-neural-radiance-for-3d#ran","syntology_url":"https://syntology.ai/paper/2304.01172","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01172"}},"official":{"repos":["virobo-15/gmnr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/polarity-is-all-you-need-to-learn-and","slug":"polarity-is-all-you-need-to-learn-and","title":"Polarity is all you need to learn and transfer faster","date":"2023-03-29","arxiv_id":"2303.17589","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/polarity-is-all-you-need-to-learn-and#ran","syntology_url":"https://syntology.ai/paper/2303.17589","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.17589"}},"official":{"repos":["aliceqingyangwang/weightpolarityexpr"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/efficientad-accurate-visual-anomaly-detection","slug":"efficientad-accurate-visual-anomaly-detection","title":"EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies","date":"2023-03-25","arxiv_id":"2303.14535","repositories_listed":33,"syntology":{"n":35,"n_ran":29,"n_constructed":0,"n_ran_checked":29,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":29,"n_pointer_only":0,"phrase":"29 ran (of which 0 constructed an object rather than computing a result; 29 with no instrument failure: 0 honoured, 0 violated, 29 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/efficientad-accurate-visual-anomaly-detection#ran","syntology_url":"https://syntology.ai/paper/2303.14535","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.14535"}},"official":null}},{"url":"/paper/siesta-efficient-online-continual-learning","slug":"siesta-efficient-online-continual-learning","title":"SIESTA: Efficient Online Continual Learning with Sleep","date":"2023-03-19","arxiv_id":"2303.10725","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/siesta-efficient-online-continual-learning#ran","syntology_url":"https://syntology.ai/paper/2303.10725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.10725"}},"official":{"repos":["yousuf907/SIESTA"],"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/biformer-vision-transformer-with-bi-level","slug":"biformer-vision-transformer-with-bi-level","title":"BiFormer: Vision Transformer with Bi-Level Routing Attention","date":"2023-03-15","arxiv_id":"2303.08810","repositories_listed":3,"syntology":{"n":6,"n_ran":6,"n_constructed":5,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 5 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/biformer-vision-transformer-with-bi-level#ran","syntology_url":"https://syntology.ai/paper/2303.08810","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.08810"}},"official":{"repos":["rayleizhu/biformer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/a-convergent-single-loop-algorithm-for","slug":"a-convergent-single-loop-algorithm-for","title":"A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data","date":"2023-03-12","arxiv_id":"2303.06595","repositories_listed":2,"syntology":{"n":20,"n_ran":8,"n_constructed":0,"n_ran_checked":3,"n_instrument":5,"n_unverified":12,"n_honours":1,"n_violates":2,"n_no_contract":0,"n_pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/a-convergent-single-loop-algorithm-for#ran","syntology_url":"https://syntology.ai/paper/2303.06595","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06595"}},"official":{"repos":["PythonOT/POT","squareroot3/gromov-wasserstein-for-graph"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":12,"ran_from_kinds":["official"]}}},{"url":"/paper/resurrecting-recurrent-neural-networks-for","slug":"resurrecting-recurrent-neural-networks-for","title":"Resurrecting Recurrent Neural Networks for Long Sequences","date":"2023-03-11","arxiv_id":"2303.06349","repositories_listed":11,"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/resurrecting-recurrent-neural-networks-for#ran","syntology_url":"https://syntology.ai/paper/2303.06349","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06349"}},"official":null}},{"url":"/paper/density-softmax-scalable-and-distance-aware","slug":"density-softmax-scalable-and-distance-aware","title":"Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts","date":"2023-02-13","arxiv_id":"2302.06495","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/density-softmax-scalable-and-distance-aware#ran","syntology_url":"https://syntology.ai/paper/2302.06495","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.06495"}},"official":{"repos":["angie-lab-jhu/density_softmax"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-practical-preferential-bayesian","slug":"towards-practical-preferential-bayesian","title":"Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes","date":"2023-02-03","arxiv_id":"2302.01513","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-practical-preferential-bayesian#ran","syntology_url":"https://syntology.ai/paper/2302.01513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.01513"}},"official":{"repos":["CyberAgentAILab/preferentialBO"],"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/audioldm-text-to-audio-generation-with-latent","slug":"audioldm-text-to-audio-generation-with-latent","title":"AudioLDM: Text-to-Audio Generation with Latent Diffusion Models","date":"2023-01-29","arxiv_id":"2301.12503","repositories_listed":4,"syntology":{"n":21,"n_ran":16,"n_constructed":1,"n_ran_checked":12,"n_instrument":4,"n_unverified":5,"n_honours":1,"n_violates":1,"n_no_contract":10,"n_pointer_only":12,"phrase":"16 ran (of which 1 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 1 violated, 10 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/audioldm-text-to-audio-generation-with-latent#ran","syntology_url":"https://syntology.ai/paper/2301.12503","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.12503"}},"official":{"repos":["haoheliu/AudioLDM"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["named_in_paper","official"]}}},{"url":"/paper/maximum-optimality-margin-a-unified-approach","slug":"maximum-optimality-margin-a-unified-approach","title":"Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming","date":"2023-01-26","arxiv_id":"2301.11260","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/maximum-optimality-margin-a-unified-approach#ran","syntology_url":"https://syntology.ai/paper/2301.11260","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11260"}},"official":{"repos":["liushangnoname/maximum-optimality-margin"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pdformer-propagation-delay-aware-dynamic-long","slug":"pdformer-propagation-delay-aware-dynamic-long","title":"PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction","date":"2023-01-19","arxiv_id":"2301.07945","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":6,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 2 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","sample_list":"/paper/pdformer-propagation-delay-aware-dynamic-long#ran","syntology_url":"https://syntology.ai/paper/2301.07945","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.07945"}},"official":{"repos":["BUAABIGSCity/PDFormer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-fullsubnet-accelerate-full-band-and-sub","slug":"fast-fullsubnet-accelerate-full-band-and-sub","title":"Fast FullSubNet: Accelerate Full-band and Sub-band Fusion Model for Single-channel Speech Enhancement","date":"2022-12-18","arxiv_id":"2212.09019","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/fast-fullsubnet-accelerate-full-band-and-sub#ran","syntology_url":"https://syntology.ai/paper/2212.09019","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.09019"}},"official":{"repos":["audio-westlakeu/fullsubnet"],"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/efficient-long-sequence-modeling-via-state","slug":"efficient-long-sequence-modeling-via-state","title":"Efficient Long Sequence Modeling via State Space Augmented Transformer","date":"2022-12-15","arxiv_id":"2212.08136","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/efficient-long-sequence-modeling-via-state#ran","syntology_url":"https://syntology.ai/paper/2212.08136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.08136"}},"official":{"repos":["microsoft/efficientlongsequencemodeling"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/phast-physics-aware-scalable-and-task","slug":"phast-physics-aware-scalable-and-task","title":"PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design","date":"2022-11-22","arxiv_id":"2211.12020","repositories_listed":2,"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/phast-physics-aware-scalable-and-task#ran","syntology_url":"https://syntology.ai/paper/2211.12020","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.12020"}},"official":{"repos":["vict0rsch/PhAST"],"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":["listed","official"]}}},{"url":"/paper/efficient-large-scale-audio-tagging-via","slug":"efficient-large-scale-audio-tagging-via","title":"Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation","date":"2022-11-09","arxiv_id":"2211.04772","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-large-scale-audio-tagging-via#ran","syntology_url":"https://syntology.ai/paper/2211.04772","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.04772"}},"official":{"repos":["fschmid56/efficientat"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/efficient-and-effective-augmentation-strategy","slug":"efficient-and-effective-augmentation-strategy","title":"Efficient and Effective Augmentation Strategy for Adversarial Training","date":"2022-10-27","arxiv_id":"2210.15318","repositories_listed":2,"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":2,"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/efficient-and-effective-augmentation-strategy#ran","syntology_url":"https://syntology.ai/paper/2210.15318","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.15318"}},"official":{"repos":["val-iisc/dajat"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/delving-into-masked-autoencoders-for-multi","slug":"delving-into-masked-autoencoders-for-multi","title":"Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification","date":"2022-10-23","arxiv_id":"2210.12843","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/delving-into-masked-autoencoders-for-multi#ran","syntology_url":"https://syntology.ai/paper/2210.12843","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12843"}},"official":{"repos":["lambert-x/medical_mae"],"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/mixture-of-attention-heads-selecting","slug":"mixture-of-attention-heads-selecting","title":"Mixture of Attention Heads: Selecting Attention Heads Per Token","date":"2022-10-11","arxiv_id":"2210.05144","repositories_listed":2,"syntology":{"n":17,"n_ran":13,"n_constructed":2,"n_ran_checked":10,"n_instrument":3,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":4,"phrase":"13 ran (of which 2 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/mixture-of-attention-heads-selecting#ran","syntology_url":"https://syntology.ai/paper/2210.05144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05144"}},"official":{"repos":["yikangshen/moa"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["found_in_text","listed","official"]}}},{"url":"/paper/concurrent-recognition-and-segmentation-with","slug":"concurrent-recognition-and-segmentation-with","title":"Learning Hierarchical Image Segmentation For Recognition and By Recognition","date":"2022-10-01","arxiv_id":"2210.00314","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/concurrent-recognition-and-segmentation-with#ran","syntology_url":"https://syntology.ai/paper/2210.00314","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.00314"}},"official":{"repos":["twke18/cast"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/inferring-subhalo-effective-density-slopes","slug":"inferring-subhalo-effective-density-slopes","title":"Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation","date":"2022-08-29","arxiv_id":"2208.13796","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/inferring-subhalo-effective-density-slopes#ran","syntology_url":"https://syntology.ai/paper/2208.13796","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.13796"}},"official":{"repos":["gemyxzhang/neural-subhalo-slope"],"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/algorithmic-differentiation-for-automatized","slug":"algorithmic-differentiation-for-automatized","title":"Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields","date":"2022-08-25","arxiv_id":"2208.12104","repositories_listed":1,"syntology":{"n":13,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/algorithmic-differentiation-for-automatized#ran","syntology_url":"https://syntology.ai/paper/2208.12104","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.12104"}},"official":{"repos":["niklasschmitz/ad-kernels"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/simplified-state-space-layers-for-sequence","slug":"simplified-state-space-layers-for-sequence","title":"Simplified State Space Layers for Sequence Modeling","date":"2022-08-09","arxiv_id":"2208.04933","repositories_listed":6,"syntology":{"n":19,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":6,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/simplified-state-space-layers-for-sequence#ran","syntology_url":"https://syntology.ai/paper/2208.04933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.04933"}},"official":{"repos":["lindermanlab/S5"],"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":["listed","official"]}}},{"url":"/paper/equivariant-hypergraph-diffusion-neural","slug":"equivariant-hypergraph-diffusion-neural","title":"Equivariant Hypergraph Diffusion Neural Operators","date":"2022-07-14","arxiv_id":"2207.06680","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/equivariant-hypergraph-diffusion-neural#ran","syntology_url":"https://syntology.ai/paper/2207.06680","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.06680"}},"official":{"repos":["graph-com/ed-hnn"],"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/efficient-aggregated-kernel-tests-using","slug":"efficient-aggregated-kernel-tests-using","title":"Efficient Aggregated Kernel Tests using Incomplete $U$-statistics","date":"2022-06-18","arxiv_id":"2206.09194","repositories_listed":4,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":4,"n_instrument":6,"n_unverified":3,"n_honours":4,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 4 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/efficient-aggregated-kernel-tests-using#ran","syntology_url":"https://syntology.ai/paper/2206.09194","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.09194"}},"official":{"repos":["antoninschrab/ksdagg"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","named_in_paper","official"]}}},{"url":"/paper/transkimmer-transformer-learns-to-layer-wise-1","slug":"transkimmer-transformer-learns-to-layer-wise-1","title":"Transkimmer: Transformer Learns to Layer-wise Skim","date":"2022-05-15","arxiv_id":"2205.07324","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/transkimmer-transformer-learns-to-layer-wise-1#ran","syntology_url":"https://syntology.ai/paper/2205.07324","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.07324"}},"official":{"repos":["chandlerguan/transkimmer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/feedback-gradient-descent-efficient-and","slug":"feedback-gradient-descent-efficient-and","title":"Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs","date":"2022-05-12","arxiv_id":"2205.08385","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/feedback-gradient-descent-efficient-and#ran","syntology_url":"https://syntology.ai/paper/2205.08385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.08385"}},"official":{"repos":["bokveizen/Feedback-Gradient-Descent"],"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/convmae-masked-convolution-meets-masked","slug":"convmae-masked-convolution-meets-masked","title":"ConvMAE: Masked Convolution Meets Masked Autoencoders","date":"2022-05-08","arxiv_id":"2205.03892","repositories_listed":5,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/convmae-masked-convolution-meets-masked#ran","syntology_url":"https://syntology.ai/paper/2205.03892","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.03892"}},"official":{"repos":["alpha-vl/convmae"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/rang-a-residual-based-adaptive-node","slug":"rang-a-residual-based-adaptive-node","title":"RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks","date":"2022-05-02","arxiv_id":"2205.01051","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rang-a-residual-based-adaptive-node#ran","syntology_url":"https://syntology.ai/paper/2205.01051","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01051"}},"official":{"repos":["weipengoo98/rang_pinn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/davit-dual-attention-vision-transformers","slug":"davit-dual-attention-vision-transformers","title":"DaViT: Dual Attention Vision Transformers","date":"2022-04-07","arxiv_id":"2204.03645","repositories_listed":4,"syntology":{"n":15,"n_ran":8,"n_constructed":5,"n_ran_checked":6,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 5 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) · 7 unverified","sample_list":"/paper/davit-dual-attention-vision-transformers#ran","syntology_url":"https://syntology.ai/paper/2204.03645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.03645"}},"official":{"repos":["dingmyu/davit"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/cryoai-amortized-inference-of-poses-for-ab","slug":"cryoai-amortized-inference-of-poses-for-ab","title":"CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images","date":"2022-03-15","arxiv_id":"2203.08138","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cryoai-amortized-inference-of-poses-for-ab#ran","syntology_url":"https://syntology.ai/paper/2203.08138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08138"}},"official":{"repos":["compspi/cryoai"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/faith-shap-the-faithful-shapley-shapley","slug":"faith-shap-the-faithful-shapley-shapley","title":"Faith-Shap: The Faithful Shapley Interaction Index","date":"2022-03-02","arxiv_id":"2203.00870","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/faith-shap-the-faithful-shapley-shapley#ran","syntology_url":"https://syntology.ai/paper/2203.00870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.00870"}},"official":null}},{"url":"/paper/guidelines-and-evaluation-for-clinical","slug":"guidelines-and-evaluation-for-clinical","title":"Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis","date":"2022-02-16","arxiv_id":"2202.10553","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/guidelines-and-evaluation-for-clinical#ran","syntology_url":"https://syntology.ai/paper/2202.10553","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.10553"}},"official":{"repos":["weinajin/multimodal_explanation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/weisfeiler-and-leman-go-infinite-spectral-and","slug":"weisfeiler-and-leman-go-infinite-spectral-and","title":"Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings","date":"2022-01-31","arxiv_id":"2201.13410","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/weisfeiler-and-leman-go-infinite-spectral-and#ran","syntology_url":"https://syntology.ai/paper/2201.13410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.13410"}},"official":{"repos":["tpfi22/spectral-and-combinatorial"],"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/prime-a-few-primitives-can-boost-robustness","slug":"prime-a-few-primitives-can-boost-robustness","title":"PRIME: A few primitives can boost robustness to common corruptions","date":"2021-12-27","arxiv_id":"2112.13547","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":3,"n_violates":1,"n_no_contract":2,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 3 honoured, 1 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/prime-a-few-primitives-can-boost-robustness#ran","syntology_url":"https://syntology.ai/paper/2112.13547","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.13547"}},"official":{"repos":["amodas/PRIME-augmentations"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mseg-a-composite-dataset-for-multi-domain-1","slug":"mseg-a-composite-dataset-for-multi-domain-1","title":"MSeg: A Composite Dataset for Multi-domain Semantic Segmentation","date":"2021-12-27","arxiv_id":"2112.13762","repositories_listed":2,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":10,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":4,"phrase":"14 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mseg-a-composite-dataset-for-multi-domain-1#ran","syntology_url":"https://syntology.ai/paper/2112.13762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.13762"}},"official":{"repos":["mseg-dataset/mseg-semantic"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/styleswin-transformer-based-gan-for-high-1","slug":"styleswin-transformer-based-gan-for-high-1","title":"StyleSwin: Transformer-based GAN for High-resolution Image Generation","date":"2021-12-20","arxiv_id":"2112.10762","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"5 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; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/styleswin-transformer-based-gan-for-high-1#ran","syntology_url":"https://syntology.ai/paper/2112.10762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.10762"}},"official":{"repos":["microsoft/StyleSwin"],"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","unlocated"]}}},{"url":"/paper/efficient-geometry-aware-3d-generative","slug":"efficient-geometry-aware-3d-generative","title":"Efficient Geometry-aware 3D Generative Adversarial Networks","date":"2021-12-15","arxiv_id":"2112.07945","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-geometry-aware-3d-generative#ran","syntology_url":"https://syntology.ai/paper/2112.07945","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.07945"}},"official":{"repos":["NVlabs/eg3d"],"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":["listed","unlocated"]}}},{"url":"/paper/tracer-extreme-attention-guided-salient","slug":"tracer-extreme-attention-guided-salient","title":"TRACER: Extreme Attention Guided Salient Object Tracing Network","date":"2021-12-14","arxiv_id":"2112.07380","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tracer-extreme-attention-guided-salient#ran","syntology_url":"https://syntology.ai/paper/2112.07380","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.07380"}},"official":{"repos":["Karel911/TRACER"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-point-transformer","slug":"fast-point-transformer","title":"Fast Point Transformer","date":"2021-12-09","arxiv_id":"2112.04702","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":1,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fast-point-transformer#ran","syntology_url":"https://syntology.ai/paper/2112.04702","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.04702"}},"official":{"repos":["POSTECH-CVLab/FastPointTransformer"],"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/sparse-detr-efficient-end-to-end-object-1","slug":"sparse-detr-efficient-end-to-end-object-1","title":"Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity","date":"2021-11-29","arxiv_id":"2111.14330","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/sparse-detr-efficient-end-to-end-object-1#ran","syntology_url":"https://syntology.ai/paper/2111.14330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.14330"}},"official":{"repos":["kakaobrain/sparse-detr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/dual-parameterization-of-sparse-variational","slug":"dual-parameterization-of-sparse-variational","title":"Dual Parameterization of Sparse Variational Gaussian Processes","date":"2021-11-05","arxiv_id":"2111.03412","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/dual-parameterization-of-sparse-variational#ran","syntology_url":"https://syntology.ai/paper/2111.03412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.03412"}},"official":{"repos":["AaltoML/t-SVGP"],"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/combining-recurrent-convolutional-and","slug":"combining-recurrent-convolutional-and","title":"Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers","date":"2021-10-26","arxiv_id":"2110.13985","repositories_listed":2,"syntology":{"n":53,"n_ran":19,"n_constructed":0,"n_ran_checked":5,"n_instrument":14,"n_unverified":34,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"19 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; 14 where Syntology's instrument failed) · 34 unverified","sample_list":"/paper/combining-recurrent-convolutional-and#ran","syntology_url":"https://syntology.ai/paper/2110.13985","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.13985"}},"official":{"repos":["hazyresearch/state-spaces"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":16,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/soft-softmax-free-transformer-with-linear","slug":"soft-softmax-free-transformer-with-linear","title":"SOFT: Softmax-free Transformer with Linear Complexity","date":"2021-10-22","arxiv_id":"2110.11945","repositories_listed":2,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/soft-softmax-free-transformer-with-linear#ran","syntology_url":"https://syntology.ai/paper/2110.11945","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.11945"}},"official":null}},{"url":"/paper/output-space-entropy-search-framework-for","slug":"output-space-entropy-search-framework-for","title":"Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization","date":"2021-10-13","arxiv_id":"2110.06980","repositories_listed":4,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/output-space-entropy-search-framework-for#ran","syntology_url":"https://syntology.ai/paper/2110.06980","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.06980"}},"official":{"repos":["belakaria/MESMO","belakaria/MESMOC","belakaria/imoca","belakaria/mf-osemo"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dropout-q-functions-for-doubly-efficient","slug":"dropout-q-functions-for-doubly-efficient","title":"Dropout Q-Functions for Doubly Efficient Reinforcement Learning","date":"2021-10-05","arxiv_id":"2110.02034","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":3,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dropout-q-functions-for-doubly-efficient#ran","syntology_url":"https://syntology.ai/paper/2110.02034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.02034"}},"official":{"repos":["TakuyaHiraoka/Dropout-Q-Functions-for-Doubly-Efficient-Reinforcement-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":["found_in_text","official"]}}},{"url":"/paper/robust-predictable-control","slug":"robust-predictable-control","title":"Robust Predictable Control","date":"2021-09-07","arxiv_id":"2109.03214","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robust-predictable-control#ran","syntology_url":"https://syntology.ai/paper/2109.03214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.03214"}},"official":{"repos":["eleurent/highway-env"],"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/an-extensible-benchmark-suite-for-learning-to","slug":"an-extensible-benchmark-suite-for-learning-to","title":"An Extensible Benchmark Suite for Learning to Simulate Physical Systems","date":"2021-08-09","arxiv_id":"2108.07799","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":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-extensible-benchmark-suite-for-learning-to#ran","syntology_url":"https://syntology.ai/paper/2108.07799","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07799"}},"official":{"repos":["karlotness/nn-benchmark"],"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/token-shift-transformer-for-video","slug":"token-shift-transformer-for-video","title":"Token Shift Transformer for Video Classification","date":"2021-08-05","arxiv_id":"2108.02432","repositories_listed":3,"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/token-shift-transformer-for-video#ran","syntology_url":"https://syntology.ai/paper/2108.02432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.02432"}},"official":{"repos":["VideoNetworks/TokShift-Transformer"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/adaptive-wavelet-distillation-from-neural","slug":"adaptive-wavelet-distillation-from-neural","title":"Adaptive wavelet distillation from neural networks through interpretations","date":"2021-07-19","arxiv_id":"2107.09145","repositories_listed":2,"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/adaptive-wavelet-distillation-from-neural#ran","syntology_url":"https://syntology.ai/paper/2107.09145","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.09145"}},"official":{"repos":["Yu-Group/adaptive-wavelets","Yu-Group/adaptive-wavelet-distillation"],"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/obtaining-better-static-word-embeddings-using","slug":"obtaining-better-static-word-embeddings-using","title":"Obtaining Better Static Word Embeddings Using Contextual Embedding Models","date":"2021-06-08","arxiv_id":"2106.04302","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/obtaining-better-static-word-embeddings-using#ran","syntology_url":"https://syntology.ai/paper/2106.04302","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04302"}},"official":{"repos":["epfml/X2Static"],"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"]}}}],"record_sha256":"1c5cd4d7b6d8fe1b9055d5b40a6a9ea79902dfb70a1ba856b019164a09e6d977","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}