{"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/image-classification/papers/ran/1","list_of":"/task/image-classification","task":"Image Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":14,"rows_per_page":100,"rows":[1,100],"of":1392,"counts":{"archive_papers_tagged":10488,"with_a_code_link":4702,"where_syntology_ran_a_sample":1392,"not_listed_spam_title":0,"listed":10488,"listed_where_code_ran":1392,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1164,"every_run_a_failure_of_syntologys_instrument":228,"listed_with_a_run_with_no_instrument_failure":1164,"listed_every_run_a_failure_of_syntologys_instrument":228,"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/image-classification/papers/ran/1","prev":null,"next":"/task/image-classification/papers/ran/2","papers":[{"url":"/paper/seqpe-transformer-with-sequential-position","slug":"seqpe-transformer-with-sequential-position","title":"SeqPE: Transformer with Sequential Position Encoding","date":"2025-06-16","arxiv_id":"2506.13277","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":12,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/seqpe-transformer-with-sequential-position#ran","syntology_url":"https://syntology.ai/paper/2506.13277","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.13277"}},"official":{"repos":["ghrua/seqpe"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-memory-efficiency-for-training-kans","slug":"improving-memory-efficiency-for-training-kans","title":"Improving Memory Efficiency for Training KANs via Meta Learning","date":"2025-06-09","arxiv_id":"2506.07549","repositories_listed":1,"syntology":{"n":9,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":9,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/improving-memory-efficiency-for-training-kans#ran","syntology_url":"https://syntology.ai/paper/2506.07549","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.07549"}},"official":{"repos":["murphyzc/metakan"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/safe-finding-sparse-and-flat-minima-to","slug":"safe-finding-sparse-and-flat-minima-to","title":"SAFE: Finding Sparse and Flat Minima to Improve Pruning","date":"2025-06-07","arxiv_id":"2506.06866","repositories_listed":2,"syntology":{"n":18,"n_ran":10,"n_constructed":1,"n_ran_checked":4,"n_instrument":6,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"10 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 6 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/safe-finding-sparse-and-flat-minima-to#ran","syntology_url":"https://syntology.ai/paper/2506.06866","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.06866"}},"official":{"repos":["LOG-postech/safe-torch","log-postech/safe-jax"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/eigenspectrum-analysis-of-neural-networks","slug":"eigenspectrum-analysis-of-neural-networks","title":"Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias","date":"2025-06-06","arxiv_id":"2506.06280","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/eigenspectrum-analysis-of-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2506.06280","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.06280"}},"official":null}},{"url":"/paper/quantifying-task-relevant-representational","slug":"quantifying-task-relevant-representational","title":"Quantifying task-relevant representational similarity using decision variable correlation","date":"2025-06-02","arxiv_id":"2506.02164","repositories_listed":0,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/quantifying-task-relevant-representational#ran","syntology_url":"https://syntology.ai/paper/2506.02164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.02164"}},"official":null}},{"url":"/paper/diagnosing-and-mitigating-modality","slug":"diagnosing-and-mitigating-modality","title":"Diagnosing and Mitigating Modality Interference in Multimodal Large Language Models","date":"2025-05-26","arxiv_id":"2505.19616","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":3,"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/diagnosing-and-mitigating-modality#ran","syntology_url":"https://syntology.ai/paper/2505.19616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.19616"}},"official":null}},{"url":"/paper/asymmetric-duos-sidekicks-improve-uncertainty","slug":"asymmetric-duos-sidekicks-improve-uncertainty","title":"Asymmetric Duos: Sidekicks Improve Uncertainty","date":"2025-05-24","arxiv_id":"2505.18636","repositories_listed":0,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/asymmetric-duos-sidekicks-improve-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2505.18636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.18636"}},"official":null}},{"url":"/paper/adaptive-temperature-scaling-with-conformal","slug":"adaptive-temperature-scaling-with-conformal","title":"Adaptive Temperature Scaling with Conformal Prediction","date":"2025-05-21","arxiv_id":"2505.15437","repositories_listed":0,"syntology":{"n":20,"n_ran":15,"n_constructed":7,"n_ran_checked":9,"n_instrument":6,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":20,"phrase":"15 ran (of which 7 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 6 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/adaptive-temperature-scaling-with-conformal#ran","syntology_url":"https://syntology.ai/paper/2505.15437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.15437"}},"official":null}},{"url":"/paper/beyond-linearity-squeeze-and-recalibrate","slug":"beyond-linearity-squeeze-and-recalibrate","title":"Beyond Linearity: Squeeze-and-Recalibrate Blocks for Few-Shot Whole Slide Image Classification","date":"2025-05-21","arxiv_id":"2505.15504","repositories_listed":0,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/beyond-linearity-squeeze-and-recalibrate#ran","syntology_url":"https://syntology.ai/paper/2505.15504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.15504"}},"official":null}},{"url":"/paper/chexworld-exploring-image-world-modeling-for","slug":"chexworld-exploring-image-world-modeling-for","title":"CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning","date":"2025-04-18","arxiv_id":"2504.13820","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/chexworld-exploring-image-world-modeling-for#ran","syntology_url":"https://syntology.ai/paper/2504.13820","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.13820"}},"official":{"repos":["LeapLabTHU/CheXWorld"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cls-rl-image-classification-with-rule-based","slug":"cls-rl-image-classification-with-rule-based","title":"Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-Tuning","date":"2025-03-20","arxiv_id":"2503.16188","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/cls-rl-image-classification-with-rule-based#ran","syntology_url":"https://syntology.ai/paper/2503.16188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.16188"}},"official":{"repos":["minglllli/CLS-RL"],"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/arc-anchored-representation-clouds-for-high","slug":"arc-anchored-representation-clouds-for-high","title":"ARC: Anchored Representation Clouds for High-Resolution INR Classification","date":"2025-03-19","arxiv_id":"2503.15156","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/arc-anchored-representation-clouds-for-high#ran","syntology_url":"https://syntology.ai/paper/2503.15156","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.15156"}},"official":{"repos":["jluij/anchored_representation_clouds"],"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/varepsilon-d-considered-harmful-best","slug":"varepsilon-d-considered-harmful-best","title":"$(\\varepsilon, δ)$ Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees","date":"2025-03-13","arxiv_id":"2503.10945","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/varepsilon-d-considered-harmful-best#ran","syntology_url":"https://syntology.ai/paper/2503.10945","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.10945"}},"official":{"repos":["Felipe-Gomez/gdp-numeric"],"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/task-vector-quantization-for-memory-efficient","slug":"task-vector-quantization-for-memory-efficient","title":"Task Vector Quantization for Memory-Efficient Model Merging","date":"2025-03-10","arxiv_id":"2503.06921","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/task-vector-quantization-for-memory-efficient#ran","syntology_url":"https://syntology.ai/paper/2503.06921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.06921"}},"official":null}},{"url":"/paper/sharpness-aware-minimization-general-analysis","slug":"sharpness-aware-minimization-general-analysis","title":"Sharpness-Aware Minimization: General Analysis and Improved Rates","date":"2025-03-04","arxiv_id":"2503.02225","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/sharpness-aware-minimization-general-analysis#ran","syntology_url":"https://syntology.ai/paper/2503.02225","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.02225"}},"official":{"repos":["dimitris-oik/unifiedsam"],"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/measurement-noise-scaling-laws-for-cellular","slug":"measurement-noise-scaling-laws-for-cellular","title":"Measurement noise scaling laws for cellular representation learning","date":"2025-03-04","arxiv_id":"2503.02726","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/measurement-noise-scaling-laws-for-cellular#ran","syntology_url":"https://syntology.ai/paper/2503.02726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.02726"}},"official":{"repos":["ggdna/scScaling"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/visual-rft-visual-reinforcement-fine-tuning","slug":"visual-rft-visual-reinforcement-fine-tuning","title":"Visual-RFT: Visual Reinforcement Fine-Tuning","date":"2025-03-03","arxiv_id":"2503.01785","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":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) · 4 unverified","sample_list":"/paper/visual-rft-visual-reinforcement-fine-tuning#ran","syntology_url":"https://syntology.ai/paper/2503.01785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.01785"}},"official":{"repos":["liuziyu77/visual-rft"],"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/transformer-meets-twicing-harnessing","slug":"transformer-meets-twicing-harnessing","title":"Transformer Meets Twicing: Harnessing Unattended Residual Information","date":"2025-03-02","arxiv_id":"2503.00687","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/transformer-meets-twicing-harnessing#ran","syntology_url":"https://syntology.ai/paper/2503.00687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.00687"}},"official":{"repos":["lazizcodes/twicing_attention"],"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/fast-and-accurate-gigapixel-pathological","slug":"fast-and-accurate-gigapixel-pathological","title":"Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learning","date":"2025-02-28","arxiv_id":"2502.21130","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":5,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":9,"phrase":"6 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fast-and-accurate-gigapixel-pathological#ran","syntology_url":"https://syntology.ai/paper/2502.21130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.21130"}},"official":{"repos":["JiuyangDong/HDMIL"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/overlock-an-overview-first-look-closely-next","slug":"overlock-an-overview-first-look-closely-next","title":"OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels","date":"2025-02-27","arxiv_id":"2502.20087","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/overlock-an-overview-first-look-closely-next#ran","syntology_url":"https://syntology.ai/paper/2502.20087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.20087"}},"official":{"repos":["lmmmeng/overlock"],"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/make-lora-great-again-boosting-lora-with","slug":"make-lora-great-again-boosting-lora-with","title":"Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment","date":"2025-02-24","arxiv_id":"2502.16894","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":3,"n_ran_checked":3,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/make-lora-great-again-boosting-lora-with#ran","syntology_url":"https://syntology.ai/paper/2502.16894","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.16894"}},"official":{"repos":["facico/goat-peft"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/maxsup-overcoming-representation-collapse-in","slug":"maxsup-overcoming-representation-collapse-in","title":"MaxSup: Overcoming Representation Collapse in Label Smoothing","date":"2025-02-18","arxiv_id":"2502.15798","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/maxsup-overcoming-representation-collapse-in#ran","syntology_url":"https://syntology.ai/paper/2502.15798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.15798"}},"official":{"repos":["zhouyuxuanyx/maximum-suppression-regularization"],"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/provably-near-optimal-federated-ensemble","slug":"provably-near-optimal-federated-ensemble","title":"Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead","date":"2025-02-10","arxiv_id":"2502.06349","repositories_listed":1,"syntology":{"n":22,"n_ran":19,"n_constructed":0,"n_ran_checked":11,"n_instrument":8,"n_unverified":3,"n_honours":5,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 5 honoured, 0 violated, 6 with no contract checked; 8 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/provably-near-optimal-federated-ensemble#ran","syntology_url":"https://syntology.ai/paper/2502.06349","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.06349"}},"official":{"repos":["pupiu45/FedGO"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/amnesia-as-a-catalyst-for-enhancing-black-box","slug":"amnesia-as-a-catalyst-for-enhancing-black-box","title":"Amnesia as a Catalyst for Enhancing Black Box Pixel Attacks in Image Classification and Object Detection","date":"2025-02-10","arxiv_id":"2502.07821","repositories_listed":1,"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":0,"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/amnesia-as-a-catalyst-for-enhancing-black-box#ran","syntology_url":"https://syntology.ai/paper/2502.07821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.07821"}},"official":{"repos":["kau-quantumailab/rfpar"],"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/training-free-neural-architecture-search","slug":"training-free-neural-architecture-search","title":"Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights","date":"2025-02-07","arxiv_id":"2502.04975","repositories_listed":1,"syntology":{"n":13,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":13,"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) · 11 unverified","sample_list":"/paper/training-free-neural-architecture-search#ran","syntology_url":"https://syntology.ai/paper/2502.04975","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.04975"}},"official":{"repos":["ondratybl/vkdnw"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/interpretable-failure-detection-with-human","slug":"interpretable-failure-detection-with-human","title":"Interpretable Failure Detection with Human-Level Concepts","date":"2025-02-07","arxiv_id":"2502.05275","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/interpretable-failure-detection-with-human#ran","syntology_url":"https://syntology.ai/paper/2502.05275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.05275"}},"official":null}},{"url":"/paper/redefining-machine-unlearning-a-conformal","slug":"redefining-machine-unlearning-a-conformal","title":"Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach","date":"2025-01-31","arxiv_id":"2501.19403","repositories_listed":0,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/redefining-machine-unlearning-a-conformal#ran","syntology_url":"https://syntology.ai/paper/2501.19403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.19403"}},"official":null}},{"url":"/paper/sidda-sinkhorn-dynamic-domain-adaptation-for","slug":"sidda-sinkhorn-dynamic-domain-adaptation-for","title":"SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks","date":"2025-01-23","arxiv_id":"2501.14048","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":3,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 3 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sidda-sinkhorn-dynamic-domain-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/2501.14048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.14048"}},"official":{"repos":["deepskies/gcnn_da","deepskies/sidda"],"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/does-vlm-classification-benefit-from-llm","slug":"does-vlm-classification-benefit-from-llm","title":"Does VLM Classification Benefit from LLM Description Semantics?","date":"2024-12-16","arxiv_id":"2412.11917","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/does-vlm-classification-benefit-from-llm#ran","syntology_url":"https://syntology.ai/paper/2412.11917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.11917"}},"official":{"repos":["compvis/disclip"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/wasserstein-distance-rivals-kullback-leibler","slug":"wasserstein-distance-rivals-kullback-leibler","title":"Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation","date":"2024-12-11","arxiv_id":"2412.08139","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":1,"n_ran_checked":1,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":7,"phrase":"6 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/wasserstein-distance-rivals-kullback-leibler#ran","syntology_url":"https://syntology.ai/paper/2412.08139","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.08139"}},"official":null}},{"url":"/paper/revisiting-weight-averaging-for-model-merging","slug":"revisiting-weight-averaging-for-model-merging","title":"Revisiting Weight Averaging for Model Merging","date":"2024-12-11","arxiv_id":"2412.12153","repositories_listed":2,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":11,"phrase":"7 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; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/revisiting-weight-averaging-for-model-merging#ran","syntology_url":"https://syntology.ai/paper/2412.12153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.12153"}},"official":{"repos":["JH-GEECS/CART_public"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/sparse-autoencoders-reveal-selective","slug":"sparse-autoencoders-reveal-selective","title":"Sparse autoencoders reveal selective remapping of visual concepts during adaptation","date":"2024-12-06","arxiv_id":"2412.05276","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/sparse-autoencoders-reveal-selective#ran","syntology_url":"https://syntology.ai/paper/2412.05276","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.05276"}},"official":{"repos":["dynamical-inference/patchsae"],"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/2dmamba-efficient-state-space-model-for-image","slug":"2dmamba-efficient-state-space-model-for-image","title":"2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification","date":"2024-12-01","arxiv_id":"2412.00678","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":1,"n_no_contract":5,"n_pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/2dmamba-efficient-state-space-model-for-image#ran","syntology_url":"https://syntology.ai/paper/2412.00678","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.00678"}},"official":{"repos":["atlasanalyticslab/2dmamba"],"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/on-the-performance-analysis-of-momentum","slug":"on-the-performance-analysis-of-momentum","title":"On the Performance Analysis of Momentum Method: A Frequency Domain Perspective","date":"2024-11-29","arxiv_id":"2411.19671","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":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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/on-the-performance-analysis-of-momentum#ran","syntology_url":"https://syntology.ai/paper/2411.19671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.19671"}},"official":null}},{"url":"/paper/munba-machine-unlearning-via-nash-bargaining","slug":"munba-machine-unlearning-via-nash-bargaining","title":"MUNBa: Machine Unlearning via Nash Bargaining","date":"2024-11-23","arxiv_id":"2411.15537","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":2,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/munba-machine-unlearning-via-nash-bargaining#ran","syntology_url":"https://syntology.ai/paper/2411.15537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.15537"}},"official":{"repos":["JingWu321/MUNBa"],"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","unlocated"]}}},{"url":"/paper/multimodal-autoregressive-pre-training-of","slug":"multimodal-autoregressive-pre-training-of","title":"Multimodal Autoregressive Pre-training of Large Vision Encoders","date":"2024-11-21","arxiv_id":"2411.14402","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multimodal-autoregressive-pre-training-of#ran","syntology_url":"https://syntology.ai/paper/2411.14402","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.14402"}},"official":{"repos":["apple/ml-aim"],"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/metala-unified-optimal-linear-approximation","slug":"metala-unified-optimal-linear-approximation","title":"MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map","date":"2024-11-16","arxiv_id":"2411.10741","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/metala-unified-optimal-linear-approximation#ran","syntology_url":"https://syntology.ai/paper/2411.10741","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.10741"}},"official":{"repos":["BICLab/MetaLA"],"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/training-objective-drives-the-consistency-of","slug":"training-objective-drives-the-consistency-of","title":"Training objective drives the consistency of representational similarity across datasets","date":"2024-11-08","arxiv_id":"2411.05561","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/training-objective-drives-the-consistency-of#ran","syntology_url":"https://syntology.ai/paper/2411.05561","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.05561"}},"official":{"repos":["lciernik/similarity_consistency"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ravl-discovering-and-mitigating-spurious","slug":"ravl-discovering-and-mitigating-spurious","title":"RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models","date":"2024-11-06","arxiv_id":"2411.04097","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/ravl-discovering-and-mitigating-spurious#ran","syntology_url":"https://syntology.ai/paper/2411.04097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.04097"}},"official":{"repos":["stanford-aimi/ravl"],"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/adopt-modified-adam-can-converge-with-any-b-2","slug":"adopt-modified-adam-can-converge-with-any-b-2","title":"ADOPT: Modified Adam Can Converge with Any $β_2$ with the Optimal Rate","date":"2024-11-05","arxiv_id":"2411.02853","repositories_listed":2,"syntology":{"n":23,"n_ran":14,"n_constructed":0,"n_ran_checked":11,"n_instrument":3,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":4,"phrase":"14 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; 3 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/adopt-modified-adam-can-converge-with-any-b-2#ran","syntology_url":"https://syntology.ai/paper/2411.02853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.02853"}},"official":{"repos":["ishohei220/adopt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/interpretable-image-classification-with-1","slug":"interpretable-image-classification-with-1","title":"Interpretable Image Classification with Adaptive Prototype-based Vision Transformers","date":"2024-10-28","arxiv_id":"2410.20722","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":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) · 1 unverified","sample_list":"/paper/interpretable-image-classification-with-1#ran","syntology_url":"https://syntology.ai/paper/2410.20722","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.20722"}},"official":{"repos":["Henrymachiyu/ProtoViT"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/frontiers-in-intelligent-colonoscopy","slug":"frontiers-in-intelligent-colonoscopy","title":"Frontiers in Intelligent Colonoscopy","date":"2024-10-22","arxiv_id":"2410.17241","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":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) · 3 unverified","sample_list":"/paper/frontiers-in-intelligent-colonoscopy#ran","syntology_url":"https://syntology.ai/paper/2410.17241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.17241"}},"official":{"repos":["ai4colonoscopy/intelliscope"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/bayesian-concept-bottleneck-models-with-llm","slug":"bayesian-concept-bottleneck-models-with-llm","title":"Bayesian Concept Bottleneck Models with LLM Priors","date":"2024-10-21","arxiv_id":"2410.15555","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bayesian-concept-bottleneck-models-with-llm#ran","syntology_url":"https://syntology.ai/paper/2410.15555","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.15555"}},"official":{"repos":["jjfeng/bc-llm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/spatial-mamba-effective-visual-state-space","slug":"spatial-mamba-effective-visual-state-space","title":"Spatial-Mamba: Effective Visual State Space Models via Structure-Aware State Fusion","date":"2024-10-19","arxiv_id":"2410.15091","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/spatial-mamba-effective-visual-state-space#ran","syntology_url":"https://syntology.ai/paper/2410.15091","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.15091"}},"official":{"repos":["edwardchasel/spatial-mamba"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/how-do-training-methods-influence-the","slug":"how-do-training-methods-influence-the","title":"How Do Training Methods Influence the Utilization of Vision Models?","date":"2024-10-18","arxiv_id":"2410.14470","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/how-do-training-methods-influence-the#ran","syntology_url":"https://syntology.ai/paper/2410.14470","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14470"}},"official":{"repos":["paulgavrikov/layer_criticality"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/loldu-low-rank-adaptation-via-lower-diag","slug":"loldu-low-rank-adaptation-via-lower-diag","title":"LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning","date":"2024-10-17","arxiv_id":"2410.13618","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/loldu-low-rank-adaptation-via-lower-diag#ran","syntology_url":"https://syntology.ai/paper/2410.13618","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13618"}},"official":{"repos":["skddj/loldu"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/interpreting-and-analyzing-clip-s-zero-shot","slug":"interpreting-and-analyzing-clip-s-zero-shot","title":"Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledge","date":"2024-10-16","arxiv_id":"2410.13016","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/interpreting-and-analyzing-clip-s-zero-shot#ran","syntology_url":"https://syntology.ai/paper/2410.13016","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13016"}},"official":{"repos":["fawazsammani/clip-interpret-mutual-knowledge"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-better-multi-head-attention-via","slug":"towards-better-multi-head-attention-via","title":"Towards Better Multi-head Attention via Channel-wise Sample Permutation","date":"2024-10-14","arxiv_id":"2410.10914","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"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) · 3 unverified","sample_list":"/paper/towards-better-multi-head-attention-via#ran","syntology_url":"https://syntology.ai/paper/2410.10914","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10914"}},"official":{"repos":["dashenzi721/csp"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/more-experts-than-galaxies-conditionally","slug":"more-experts-than-galaxies-conditionally","title":"More Experts Than Galaxies: Conditionally-overlapping Experts With Biologically-Inspired Fixed Routing","date":"2024-10-10","arxiv_id":"2410.08003","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/more-experts-than-galaxies-conditionally#ran","syntology_url":"https://syntology.ai/paper/2410.08003","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.08003"}},"official":{"repos":["shaier/comet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bilinear-mlps-enable-weight-based-mechanistic","slug":"bilinear-mlps-enable-weight-based-mechanistic","title":"Bilinear MLPs enable weight-based mechanistic interpretability","date":"2024-10-10","arxiv_id":"2410.08417","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"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: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bilinear-mlps-enable-weight-based-mechanistic#ran","syntology_url":"https://syntology.ai/paper/2410.08417","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.08417"}},"official":{"repos":["tdooms/bilinear-decomposition"],"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/one-initialization-to-rule-them-all-fine","slug":"one-initialization-to-rule-them-all-fine","title":"Parameter Efficient Fine-tuning via Explained Variance Adaptation","date":"2024-10-09","arxiv_id":"2410.07170","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":1,"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, 1 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/one-initialization-to-rule-them-all-fine#ran","syntology_url":"https://syntology.ai/paper/2410.07170","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.07170"}},"official":{"repos":["BenediktAlkin/vtab1k-pytorch","ml-jku/EVA"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/negmerge-consensual-weight-negation-for","slug":"negmerge-consensual-weight-negation-for","title":"NegMerge: Consensual Weight Negation for Strong Machine Unlearning","date":"2024-10-08","arxiv_id":"2410.05583","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/negmerge-consensual-weight-negation-for#ran","syntology_url":"https://syntology.ai/paper/2410.05583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.05583"}},"official":{"repos":["naver-ai/negmerge"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/stochastic-kernel-regularisation-improves","slug":"stochastic-kernel-regularisation-improves","title":"Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines","date":"2024-10-08","arxiv_id":"2410.06171","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":3,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stochastic-kernel-regularisation-improves#ran","syntology_url":"https://syntology.ai/paper/2410.06171","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.06171"}},"official":{"repos":["edwardmilsom/skr_cdkm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/conformal-structured-prediction","slug":"conformal-structured-prediction","title":"Conformal Structured Prediction","date":"2024-10-08","arxiv_id":"2410.06296","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/conformal-structured-prediction#ran","syntology_url":"https://syntology.ai/paper/2410.06296","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.06296"}},"official":{"repos":["botong516/conformal_structured_prediction"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/select-a-large-scale-benchmark-of-data","slug":"select-a-large-scale-benchmark-of-data","title":"SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification","date":"2024-10-07","arxiv_id":"2410.05057","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/select-a-large-scale-benchmark-of-data#ran","syntology_url":"https://syntology.ai/paper/2410.05057","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.05057"}},"official":{"repos":["jimmyxu123/select"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/interpret-your-decision-logical-reasoning","slug":"interpret-your-decision-logical-reasoning","title":"Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification","date":"2024-10-06","arxiv_id":"2410.04492","repositories_listed":1,"syntology":{"n":16,"n_ran":16,"n_constructed":0,"n_ran_checked":15,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":16,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/interpret-your-decision-logical-reasoning#ran","syntology_url":"https://syntology.ai/paper/2410.04492","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04492"}},"official":{"repos":["zhaorui-tan/L-Reg_NeurIPS24"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/model-developmental-safety-a-safety-centric","slug":"model-developmental-safety-a-safety-centric","title":"A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety","date":"2024-10-04","arxiv_id":"2410.03955","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":9,"phrase":"5 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; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/model-developmental-safety-a-safety-centric#ran","syntology_url":"https://syntology.ai/paper/2410.03955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.03955"}},"official":{"repos":["ganglii/devsafety"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/all-in-one-image-coding-for-joint-human","slug":"all-in-one-image-coding-for-joint-human","title":"All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation","date":"2024-09-29","arxiv_id":"2409.19660","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":7,"n_instrument":5,"n_unverified":3,"n_honours":2,"n_violates":1,"n_no_contract":4,"n_pointer_only":15,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 1 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/all-in-one-image-coding-for-joint-human#ran","syntology_url":"https://syntology.ai/paper/2409.19660","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19660"}},"official":{"repos":["NJUVISION/MPA"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/vision-language-models-are-strong-noisy-label","slug":"vision-language-models-are-strong-noisy-label","title":"Vision-Language Models are Strong Noisy Label Detectors","date":"2024-09-29","arxiv_id":"2409.19696","repositories_listed":1,"syntology":{"n":8,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/vision-language-models-are-strong-noisy-label#ran","syntology_url":"https://syntology.ai/paper/2409.19696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19696"}},"official":{"repos":["HotanLee/DeFT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-a-dual-tier-few-shot-learning-paradigm","slug":"fast-a-dual-tier-few-shot-learning-paradigm","title":"FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification","date":"2024-09-29","arxiv_id":"2409.19720","repositories_listed":1,"syntology":{"n":17,"n_ran":14,"n_constructed":1,"n_ran_checked":12,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":17,"phrase":"14 ran (of which 1 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fast-a-dual-tier-few-shot-learning-paradigm#ran","syntology_url":"https://syntology.ai/paper/2409.19720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19720"}},"official":{"repos":["fukexue/fast"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":1,"n_ran_no_instrument_failure":12,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-obstruct-few-shot-image","slug":"learning-to-obstruct-few-shot-image","title":"Learning to Obstruct Few-Shot Image Classification over Restricted Classes","date":"2024-09-28","arxiv_id":"2409.19210","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/learning-to-obstruct-few-shot-image#ran","syntology_url":"https://syntology.ai/paper/2409.19210","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19210"}},"official":{"repos":["amberyzheng/LTO"],"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/clip-moe-towards-building-mixture-of-experts","slug":"clip-moe-towards-building-mixture-of-experts","title":"CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling","date":"2024-09-28","arxiv_id":"2409.19291","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/clip-moe-towards-building-mixture-of-experts#ran","syntology_url":"https://syntology.ai/paper/2409.19291","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19291"}},"official":{"repos":["OpenSparseLLMs/CLIP-MoE"],"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/realistic-evaluation-of-model-merging-for","slug":"realistic-evaluation-of-model-merging-for","title":"Realistic Evaluation of Model Merging for Compositional Generalization","date":"2024-09-26","arxiv_id":"2409.18314","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/realistic-evaluation-of-model-merging-for#ran","syntology_url":"https://syntology.ai/paper/2409.18314","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.18314"}},"official":{"repos":["r-three/realistic_evaluation_of_model_merging_for_compositional_generalization"],"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/unraveling-the-hessian-a-key-to-smooth","slug":"unraveling-the-hessian-a-key-to-smooth","title":"Unraveling the Hessian: A Key to Smooth Convergence in Loss Function Landscapes","date":"2024-09-18","arxiv_id":"2409.11995","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unraveling-the-hessian-a-key-to-smooth#ran","syntology_url":"https://syntology.ai/paper/2409.11995","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.11995"}},"official":{"repos":["kisnikser/landscape-hessian"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/kolmogorov-arnold-transformer","slug":"kolmogorov-arnold-transformer","title":"Kolmogorov-Arnold Transformer","date":"2024-09-16","arxiv_id":"2409.10594","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/kolmogorov-arnold-transformer#ran","syntology_url":"https://syntology.ai/paper/2409.10594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.10594"}},"official":{"repos":["Adamdad/kat"],"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/token-turing-machines-are-efficient-vision","slug":"token-turing-machines-are-efficient-vision","title":"Token Turing Machines are Efficient Vision Models","date":"2024-09-11","arxiv_id":"2409.07613","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/token-turing-machines-are-efficient-vision#ran","syntology_url":"https://syntology.ai/paper/2409.07613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.07613"}},"official":{"repos":["pjjajal/efficientttms"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/entaugment-entropy-driven-adaptive-data","slug":"entaugment-entropy-driven-adaptive-data","title":"EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification","date":"2024-09-10","arxiv_id":"2409.06290","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":10,"phrase":"7 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/entaugment-entropy-driven-adaptive-data#ran","syntology_url":"https://syntology.ai/paper/2409.06290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.06290"}},"official":{"repos":["jackbrocp/entaugment"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-attacks-on-data-attribution","slug":"adversarial-attacks-on-data-attribution","title":"Adversarial Attacks on Data Attribution","date":"2024-09-09","arxiv_id":"2409.05657","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adversarial-attacks-on-data-attribution#ran","syntology_url":"https://syntology.ai/paper/2409.05657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.05657"}},"official":{"repos":["trais-lab/adversarial-attack-data-attribution"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-ademamix-optimizer-better-faster-older","slug":"the-ademamix-optimizer-better-faster-older","title":"The AdEMAMix Optimizer: Better, Faster, Older","date":"2024-09-05","arxiv_id":"2409.03137","repositories_listed":4,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-ademamix-optimizer-better-faster-older#ran","syntology_url":"https://syntology.ai/paper/2409.03137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.03137"}},"official":{"repos":["apple/ml-ademamix"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/training-free-conversion-of-pretrained-anns","slug":"training-free-conversion-of-pretrained-anns","title":"Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications","date":"2024-09-05","arxiv_id":"2409.03368","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"4 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/training-free-conversion-of-pretrained-anns#ran","syntology_url":"https://syntology.ai/paper/2409.03368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.03368"}},"official":{"repos":["putshua/inference-scale-ann-snn"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/lowformer-hardware-efficient-design-for","slug":"lowformer-hardware-efficient-design-for","title":"LowFormer: Hardware Efficient Design for Convolutional Transformer Backbones","date":"2024-09-05","arxiv_id":"2409.03460","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/lowformer-hardware-efficient-design-for#ran","syntology_url":"https://syntology.ai/paper/2409.03460","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.03460"}},"official":{"repos":["altair199797/lowformer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/spatial-aware-conformal-prediction-for","slug":"spatial-aware-conformal-prediction-for","title":"Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification","date":"2024-09-02","arxiv_id":"2409.01236","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/spatial-aware-conformal-prediction-for#ran","syntology_url":"https://syntology.ai/paper/2409.01236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.01236"}},"official":{"repos":["j4ckliu/sacp"],"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/improving-diffusion-based-data-augmentation","slug":"improving-diffusion-based-data-augmentation","title":"Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification","date":"2024-08-29","arxiv_id":"2408.16266","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":13,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/improving-diffusion-based-data-augmentation#ran","syntology_url":"https://syntology.ai/paper/2408.16266","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.16266"}},"official":{"repos":["scuwyh2000/diff-ii"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/mscpt-few-shot-whole-slide-image","slug":"mscpt-few-shot-whole-slide-image","title":"MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning","date":"2024-08-21","arxiv_id":"2408.11505","repositories_listed":1,"syntology":{"n":8,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":8,"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) · 6 unverified","sample_list":"/paper/mscpt-few-shot-whole-slide-image#ran","syntology_url":"https://syntology.ai/paper/2408.11505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.11505"}},"official":{"repos":["hanminghao/mscpt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/approaching-deep-learning-through-the","slug":"approaching-deep-learning-through-the","title":"Approaching Deep Learning through the Spectral Dynamics of Weights","date":"2024-08-21","arxiv_id":"2408.11804","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/approaching-deep-learning-through-the#ran","syntology_url":"https://syntology.ai/paper/2408.11804","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.11804"}},"official":{"repos":["dyunis/spectral_dynamics"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-image-to-image-diffusion-classifier","slug":"efficient-image-to-image-diffusion-classifier","title":"Efficient Image-to-Image Diffusion Classifier for Adversarial Robustness","date":"2024-08-16","arxiv_id":"2408.08502","repositories_listed":1,"syntology":{"n":18,"n_ran":16,"n_constructed":0,"n_ran_checked":7,"n_instrument":9,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":5,"n_pointer_only":18,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 1 violated, 5 with no contract checked; 9 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/efficient-image-to-image-diffusion-classifier#ran","syntology_url":"https://syntology.ai/paper/2408.08502","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.08502"}},"official":{"repos":["hfmei/idc"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hair-hypernetworks-based-all-in-one-image","slug":"hair-hypernetworks-based-all-in-one-image","title":"HAIR: Hypernetworks-based All-in-One Image Restoration","date":"2024-08-15","arxiv_id":"2408.08091","repositories_listed":1,"syntology":{"n":18,"n_ran":13,"n_constructed":0,"n_ran_checked":9,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":18,"phrase":"13 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; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/hair-hypernetworks-based-all-in-one-image#ran","syntology_url":"https://syntology.ai/paper/2408.08091","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.08091"}},"official":{"repos":["toummHus/HAIR"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/slca-unleash-the-power-of-sequential-fine","slug":"slca-unleash-the-power-of-sequential-fine","title":"SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training","date":"2024-08-15","arxiv_id":"2408.08295","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"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; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/slca-unleash-the-power-of-sequential-fine#ran","syntology_url":"https://syntology.ai/paper/2408.08295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.08295"}},"official":{"repos":["gengdavid/slca"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cas-vit-convolutional-additive-self-attention","slug":"cas-vit-convolutional-additive-self-attention","title":"CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications","date":"2024-08-07","arxiv_id":"2408.03703","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cas-vit-convolutional-additive-self-attention#ran","syntology_url":"https://syntology.ai/paper/2408.03703","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.03703"}},"official":{"repos":["tianfang-zhang/cas-vit"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/language-driven-slice-discovery-and-error","slug":"language-driven-slice-discovery-and-error","title":"LADDER: Language Driven Slice Discovery and Error Rectification","date":"2024-07-31","arxiv_id":"2408.07832","repositories_listed":1,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":17,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/language-driven-slice-discovery-and-error#ran","syntology_url":"https://syntology.ai/paper/2408.07832","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.07832"}},"official":null}},{"url":"/paper/diffusion-feedback-helps-clip-see-better","slug":"diffusion-feedback-helps-clip-see-better","title":"Diffusion Feedback Helps CLIP See Better","date":"2024-07-29","arxiv_id":"2407.20171","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/diffusion-feedback-helps-clip-see-better#ran","syntology_url":"https://syntology.ai/paper/2407.20171","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.20171"}},"official":{"repos":["baaivision/diva"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/depth-wise-convolutions-in-vision","slug":"depth-wise-convolutions-in-vision","title":"Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small Datasets","date":"2024-07-28","arxiv_id":"2407.19394","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":2,"n_no_contract":8,"n_pointer_only":16,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 2 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/depth-wise-convolutions-in-vision#ran","syntology_url":"https://syntology.ai/paper/2407.19394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.19394"}},"official":{"repos":["ztx-100/efficient_vit_with_dw"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/vssd-vision-mamba-with-non-casual-state-space","slug":"vssd-vision-mamba-with-non-casual-state-space","title":"VSSD: Vision Mamba with Non-Causal State Space Duality","date":"2024-07-26","arxiv_id":"2407.18559","repositories_listed":2,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/vssd-vision-mamba-with-non-casual-state-space#ran","syntology_url":"https://syntology.ai/paper/2407.18559","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.18559"}},"official":{"repos":["yuhengsss/vssd"],"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/mew-multiplexed-immunofluorescence-image","slug":"mew-multiplexed-immunofluorescence-image","title":"Mew: Multiplexed Immunofluorescence Image Analysis through an Efficient Multiplex Network","date":"2024-07-25","arxiv_id":"2407.17857","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":11,"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) · 0 unverified","sample_list":"/paper/mew-multiplexed-immunofluorescence-image#ran","syntology_url":"https://syntology.ai/paper/2407.17857","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.17857"}},"official":{"repos":["unites-lab/mew"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lora-pro-are-low-rank-adapters-properly","slug":"lora-pro-are-low-rank-adapters-properly","title":"LoRA-Pro: Are Low-Rank Adapters Properly Optimized?","date":"2024-07-25","arxiv_id":"2407.18242","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":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/lora-pro-are-low-rank-adapters-properly#ran","syntology_url":"https://syntology.ai/paper/2407.18242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.18242"}},"official":{"repos":["mrflogs/LoRA-Pro"],"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/is-user-feedback-always-informative-retrieval","slug":"is-user-feedback-always-informative-retrieval","title":"Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source Data","date":"2024-07-22","arxiv_id":"2407.15383","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/is-user-feedback-always-informative-retrieval#ran","syntology_url":"https://syntology.ai/paper/2407.15383","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.15383"}},"official":{"repos":["junha1125/rld-semisda"],"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/benchmarking-robust-self-supervised-learning","slug":"benchmarking-robust-self-supervised-learning","title":"Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks","date":"2024-07-17","arxiv_id":"2407.12588","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/benchmarking-robust-self-supervised-learning#ran","syntology_url":"https://syntology.ai/paper/2407.12588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.12588"}},"official":{"repos":["layer6ai-labs/ssl-robustness"],"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/datadream-few-shot-guided-dataset-generation","slug":"datadream-few-shot-guided-dataset-generation","title":"DataDream: Few-shot Guided Dataset Generation","date":"2024-07-15","arxiv_id":"2407.10910","repositories_listed":2,"syntology":{"n":21,"n_ran":17,"n_constructed":0,"n_ran_checked":16,"n_instrument":1,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":15,"n_pointer_only":21,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 1 honoured, 0 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/datadream-few-shot-guided-dataset-generation#ran","syntology_url":"https://syntology.ai/paper/2407.10910","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.10910"}},"official":{"repos":["explainableml/datadream"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/dual-stage-hyperspectral-image-classification","slug":"dual-stage-hyperspectral-image-classification","title":"Dual-stage Hyperspectral Image Classification Model with Spectral Supertoken","date":"2024-07-10","arxiv_id":"2407.07307","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dual-stage-hyperspectral-image-classification#ran","syntology_url":"https://syntology.ai/paper/2407.07307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07307"}},"official":{"repos":["laprf/dstc"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/trainable-highly-expressive-activation","slug":"trainable-highly-expressive-activation","title":"Trainable Highly-expressive Activation Functions","date":"2024-07-10","arxiv_id":"2407.07564","repositories_listed":1,"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/trainable-highly-expressive-activation#ran","syntology_url":"https://syntology.ai/paper/2407.07564","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07564"}},"official":{"repos":["bgu-cs-vil/ditac"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mambavision-a-hybrid-mamba-transformer-vision","slug":"mambavision-a-hybrid-mamba-transformer-vision","title":"MambaVision: A Hybrid Mamba-Transformer Vision Backbone","date":"2024-07-10","arxiv_id":"2407.08083","repositories_listed":3,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"4 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; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/mambavision-a-hybrid-mamba-transformer-vision#ran","syntology_url":"https://syntology.ai/paper/2407.08083","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.08083"}},"official":{"repos":["nvlabs/mambavision"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/wavelet-convolutions-for-large-receptive","slug":"wavelet-convolutions-for-large-receptive","title":"Wavelet Convolutions for Large Receptive Fields","date":"2024-07-08","arxiv_id":"2407.05848","repositories_listed":1,"syntology":{"n":14,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/wavelet-convolutions-for-large-receptive#ran","syntology_url":"https://syntology.ai/paper/2407.05848","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05848"}},"official":{"repos":["bgu-cs-vil/wtconv"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-probability-aggregation-clustering","slug":"deep-probability-aggregation-clustering","title":"Deep Online Probability Aggregation Clustering","date":"2024-07-07","arxiv_id":"2407.05246","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"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) · 3 unverified","sample_list":"/paper/deep-probability-aggregation-clustering#ran","syntology_url":"https://syntology.ai/paper/2407.05246","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05246"}},"official":{"repos":["aomandechenai/deep-probability-aggregation-clustering"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/leveraging-topological-guidance-for-improved","slug":"leveraging-topological-guidance-for-improved","title":"Leveraging Topological Guidance for Improved Knowledge Distillation","date":"2024-07-07","arxiv_id":"2407.05316","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":11,"n_instrument":3,"n_unverified":2,"n_honours":2,"n_violates":2,"n_no_contract":7,"n_pointer_only":16,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 2 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/leveraging-topological-guidance-for-improved#ran","syntology_url":"https://syntology.ai/paper/2407.05316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05316"}},"official":{"repos":["jeunsom/TGD"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pdiscoformer-relaxing-part-discovery","slug":"pdiscoformer-relaxing-part-discovery","title":"PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers","date":"2024-07-05","arxiv_id":"2407.04538","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pdiscoformer-relaxing-part-discovery#ran","syntology_url":"https://syntology.ai/paper/2407.04538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.04538"}},"official":{"repos":["ananthu-aniraj/pdiscoformer"],"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/awt-transferring-vision-language-models-via","slug":"awt-transferring-vision-language-models-via","title":"AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation","date":"2024-07-05","arxiv_id":"2407.04603","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/awt-transferring-vision-language-models-via#ran","syntology_url":"https://syntology.ai/paper/2407.04603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.04603"}},"official":{"repos":["MCG-NJU/AWT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dgr-mil-exploring-diverse-global","slug":"dgr-mil-exploring-diverse-global","title":"DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification","date":"2024-07-04","arxiv_id":"2407.03575","repositories_listed":1,"syntology":{"n":9,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/dgr-mil-exploring-diverse-global#ran","syntology_url":"https://syntology.ai/paper/2407.03575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.03575"}},"official":{"repos":["chongqingnosubway/dgr-mil"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/kolmogorov-arnold-convolutions-design","slug":"kolmogorov-arnold-convolutions-design","title":"Kolmogorov-Arnold Convolutions: Design Principles and Empirical Studies","date":"2024-07-01","arxiv_id":"2407.01092","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/kolmogorov-arnold-convolutions-design#ran","syntology_url":"https://syntology.ai/paper/2407.01092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.01092"}},"official":{"repos":["ivandrokin/torch-conv-kan"],"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/increasing-model-capacity-for-free-a-simple","slug":"increasing-model-capacity-for-free-a-simple","title":"Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning","date":"2024-07-01","arxiv_id":"2407.01320","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"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) · 3 unverified","sample_list":"/paper/increasing-model-capacity-for-free-a-simple#ran","syntology_url":"https://syntology.ai/paper/2407.01320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.01320"}},"official":{"repos":["lins-lab/capaboost"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/gallop-learning-global-and-local-prompts-for","slug":"gallop-learning-global-and-local-prompts-for","title":"GalLoP: Learning Global and Local Prompts for Vision-Language Models","date":"2024-07-01","arxiv_id":"2407.01400","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/gallop-learning-global-and-local-prompts-for#ran","syntology_url":"https://syntology.ai/paper/2407.01400","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.01400"}},"official":{"repos":["marclafon/gallop"],"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"]}}}],"record_sha256":"54d7ab49e69f80b28341d12ecdc0cc54502fdc65342f366a6ae2abc0c3638366","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}