{"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/outlier-detection/papers/ran/1","list_of":"/task/outlier-detection","task":"Outlier Detection","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":1,"rows_per_page":100,"rows":[1,57],"of":57,"counts":{"archive_papers_tagged":703,"with_a_code_link":234,"where_syntology_ran_a_sample":57,"not_listed_spam_title":0,"listed":703,"listed_where_code_ran":57,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":49,"every_run_a_failure_of_syntologys_instrument":8,"listed_with_a_run_with_no_instrument_failure":49,"listed_every_run_a_failure_of_syntologys_instrument":8,"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/outlier-detection/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/benign-samples-matter-fine-tuning-on-outlier","slug":"benign-samples-matter-fine-tuning-on-outlier","title":"Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety","date":"2025-05-11","arxiv_id":"2505.06843","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/benign-samples-matter-fine-tuning-on-outlier#ran","syntology_url":"https://syntology.ai/paper/2505.06843","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.06843"}},"official":{"repos":["guanzihan/benign-samples-matter"],"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/pyod-2-a-python-library-for-outlier-detection","slug":"pyod-2-a-python-library-for-outlier-detection","title":"PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection","date":"2024-12-11","arxiv_id":"2412.12154","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/pyod-2-a-python-library-for-outlier-detection#ran","syntology_url":"https://syntology.ai/paper/2412.12154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.12154"}},"official":{"repos":["yzhao062/pyod"],"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/enhancing-diversity-in-bayesian-deep-learning","slug":"enhancing-diversity-in-bayesian-deep-learning","title":"Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA","date":"2024-10-31","arxiv_id":"2411.00259","repositories_listed":1,"syntology":{"n":19,"n_ran":7,"n_constructed":1,"n_ran_checked":2,"n_instrument":5,"n_unverified":12,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"7 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/enhancing-diversity-in-bayesian-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2411.00259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.00259"}},"official":{"repos":["Deep-Machine-Vision/he-cka-ensembles"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":12,"ran_from_kinds":["official"]}}},{"url":"/paper/stamp-outlier-aware-test-time-adaptation-with","slug":"stamp-outlier-aware-test-time-adaptation-with","title":"STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay","date":"2024-07-22","arxiv_id":"2407.15773","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stamp-outlier-aware-test-time-adaptation-with#ran","syntology_url":"https://syntology.ai/paper/2407.15773","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.15773"}},"official":{"repos":["yuyongcan/stamp"],"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/rethinking-unsupervised-outlier-detection-via","slug":"rethinking-unsupervised-outlier-detection-via","title":"Rethinking Unsupervised Outlier Detection via Multiple Thresholding","date":"2024-07-07","arxiv_id":"2407.05382","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rethinking-unsupervised-outlier-detection-via#ran","syntology_url":"https://syntology.ai/paper/2407.05382","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05382"}},"official":{"repos":["zhliu-uod/multi-t","doudouhhh/Multi-T"],"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/rethinking-graph-backdoor-attacks-a","slug":"rethinking-graph-backdoor-attacks-a","title":"Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective","date":"2024-05-17","arxiv_id":"2405.10757","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rethinking-graph-backdoor-attacks-a#ran","syntology_url":"https://syntology.ai/paper/2405.10757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.10757"}},"official":{"repos":["zzwjames/dpgba"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/interactive-continual-learning-fast-and-slow","slug":"interactive-continual-learning-fast-and-slow","title":"Interactive Continual Learning: Fast and Slow Thinking","date":"2024-03-05","arxiv_id":"2403.02628","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/interactive-continual-learning-fast-and-slow#ran","syntology_url":"https://syntology.ai/paper/2403.02628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.02628"}},"official":{"repos":["biqing-qi/interactive-continual-learning-fast-and-slow-thinking"],"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/data-augmentation-for-supervised-graph","slug":"data-augmentation-for-supervised-graph","title":"Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models","date":"2023-12-29","arxiv_id":"2312.17679","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/data-augmentation-for-supervised-graph#ran","syntology_url":"https://syntology.ai/paper/2312.17679","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.17679"}},"official":{"repos":["kayzliu/godm"],"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/ssb-simple-but-strong-baseline-for-boosting-1","slug":"ssb-simple-but-strong-baseline-for-boosting-1","title":"SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning","date":"2023-11-17","arxiv_id":"2311.10572","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":2,"n_ran_checked":4,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":12,"phrase":"7 ran (of which 2 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) · 5 unverified","sample_list":"/paper/ssb-simple-but-strong-baseline-for-boosting-1#ran","syntology_url":"https://syntology.ai/paper/2311.10572","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.10572"}},"official":{"repos":["yue-fan/ssb"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-on-graphs-with-out-of-distribution","slug":"learning-on-graphs-with-out-of-distribution","title":"Learning on Graphs with Out-of-Distribution Nodes","date":"2023-08-13","arxiv_id":"2308.06714","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/learning-on-graphs-with-out-of-distribution#ran","syntology_url":"https://syntology.ai/paper/2308.06714","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06714"}},"official":{"repos":["songyyyy/kdd22-oodgat"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-unsupervised-deep-outlier-model","slug":"fast-unsupervised-deep-outlier-model","title":"Fast Unsupervised Deep Outlier Model Selection with Hypernetworks","date":"2023-07-20","arxiv_id":"2307.10529","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fast-unsupervised-deep-outlier-model#ran","syntology_url":"https://syntology.ai/paper/2307.10529","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.10529"}},"official":{"repos":["xyvivian/hyper"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/computationally-assisted-quality-control-for","slug":"computationally-assisted-quality-control-for","title":"Computationally Assisted Quality Control for Public Health Data Streams","date":"2023-06-29","arxiv_id":"2306.16914","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/computationally-assisted-quality-control-for#ran","syntology_url":"https://syntology.ai/paper/2306.16914","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.16914"}},"official":{"repos":["ananya-joshi/ijcai23_supplemental","cmu-delphi/covidcast-indicators"],"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/anomaly-detection-in-networks-via-score-based","slug":"anomaly-detection-in-networks-via-score-based","title":"Anomaly Detection in Networks via Score-Based Generative Models","date":"2023-06-27","arxiv_id":"2306.15324","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/anomaly-detection-in-networks-via-score-based#ran","syntology_url":"https://syntology.ai/paper/2306.15324","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.15324"}},"official":{"repos":["realfolkcode/graphdiffusionanomaly"],"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/conformal-inference-is-almost-free-for-neural","slug":"conformal-inference-is-almost-free-for-neural","title":"Conformal inference is (almost) free for neural networks trained with early stopping","date":"2023-01-27","arxiv_id":"2301.11556","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/conformal-inference-is-almost-free-for-neural#ran","syntology_url":"https://syntology.ai/paper/2301.11556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11556"}},"official":{"repos":["ziyiliang/conformalized_early_stopping"],"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/odim-an-efficient-method-to-detect-outliers","slug":"odim-an-efficient-method-to-detect-outliers","title":"ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models","date":"2023-01-11","arxiv_id":"2301.04257","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/odim-an-efficient-method-to-detect-outliers#ran","syntology_url":"https://syntology.ai/paper/2301.04257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.04257"}},"official":{"repos":["jshwang0311/odim"],"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/pixels-together-strong-segmenting-unknown","slug":"pixels-together-strong-segmenting-unknown","title":"RbA: Segmenting Unknown Regions Rejected by All","date":"2022-11-25","arxiv_id":"2211.14293","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pixels-together-strong-segmenting-unknown#ran","syntology_url":"https://syntology.ai/paper/2211.14293","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.14293"}},"official":{"repos":["NazirNayal8/RbA"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/toward-unsupervised-outlier-model-selection","slug":"toward-unsupervised-outlier-model-selection","title":"Toward Unsupervised Outlier Model Selection","date":"2022-11-03","arxiv_id":"2211.01834","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/toward-unsupervised-outlier-model-selection#ran","syntology_url":"https://syntology.ai/paper/2211.01834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.01834"}},"official":{"repos":["yzhao062/elect"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/are-we-really-making-much-progress-in","slug":"are-we-really-making-much-progress-in","title":"Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method","date":"2022-10-24","arxiv_id":"2210.12941","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":3,"n_no_contract":0,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 3 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/are-we-really-making-much-progress-in#ran","syntology_url":"https://syntology.ai/paper/2210.12941","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12941"}},"official":{"repos":["goldennormal/vgod-github"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/analyzing-the-robustness-of-pecnet","slug":"analyzing-the-robustness-of-pecnet","title":"G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System","date":"2022-10-15","arxiv_id":"2210.09846","repositories_listed":1,"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/analyzing-the-robustness-of-pecnet#ran","syntology_url":"https://syntology.ai/paper/2210.09846","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09846"}},"official":{"repos":["aryan-garg/pecnet-pedestrian-trajectory-prediction"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/eco-tr-efficient-correspondences-finding-via","slug":"eco-tr-efficient-correspondences-finding-via","title":"ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement","date":"2022-09-25","arxiv_id":"2209.12213","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"phrase":"11 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/eco-tr-efficient-correspondences-finding-via#ran","syntology_url":"https://syntology.ai/paper/2209.12213","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.12213"}},"official":null}},{"url":"/paper/outlier-detection-using-self-organizing-maps","slug":"outlier-detection-using-self-organizing-maps","title":"Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis","date":"2022-08-18","arxiv_id":"2208.08834","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/outlier-detection-using-self-organizing-maps#ran","syntology_url":"https://syntology.ai/paper/2208.08834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.08834"}},"official":null}},{"url":"/paper/repairing-systematic-outliers-by-learning-1","slug":"repairing-systematic-outliers-by-learning-1","title":"Repairing Systematic Outliers by Learning Clean Subspaces in VAEs","date":"2022-07-17","arxiv_id":"2207.08050","repositories_listed":1,"syntology":{"n":20,"n_ran":16,"n_constructed":5,"n_ran_checked":6,"n_instrument":10,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"16 ran (of which 5 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 10 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/repairing-systematic-outliers-by-learning-1#ran","syntology_url":"https://syntology.ai/paper/2207.08050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.08050"}},"official":{"repos":["sfme/clsvae-error-repair"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/adbench-anomaly-detection-benchmark","slug":"adbench-anomaly-detection-benchmark","title":"ADBench: Anomaly Detection Benchmark","date":"2022-06-19","arxiv_id":"2206.09426","repositories_listed":5,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adbench-anomaly-detection-benchmark#ran","syntology_url":"https://syntology.ai/paper/2206.09426","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.09426"}},"official":{"repos":["minqi824/adbench"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/automatically-detecting-anomalous-exoplanet","slug":"automatically-detecting-anomalous-exoplanet","title":"Automatically detecting anomalous exoplanet transits","date":"2021-11-16","arxiv_id":"2111.08679","repositories_listed":1,"syntology":{"n":25,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":13,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"12 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; 1 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/automatically-detecting-anomalous-exoplanet#ran","syntology_url":"https://syntology.ai/paper/2111.08679","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.08679"}},"official":{"repos":["christophhoenes/anomalousexoplanettransits"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":13,"ran_from_kinds":["official"]}}},{"url":"/paper/the-magnitude-vector-of-images-1","slug":"the-magnitude-vector-of-images-1","title":"The magnitude vector of images","date":"2021-10-28","arxiv_id":"2110.15188","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/the-magnitude-vector-of-images-1#ran","syntology_url":"https://syntology.ai/paper/2110.15188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.15188"}},"official":{"repos":["mikeadamer/mag-metric"],"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/tod-tensor-based-outlier-detection","slug":"tod-tensor-based-outlier-detection","title":"TOD: GPU-accelerated Outlier Detection via Tensor Operations","date":"2021-10-26","arxiv_id":"2110.14007","repositories_listed":2,"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":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) · 3 unverified","sample_list":"/paper/tod-tensor-based-outlier-detection#ran","syntology_url":"https://syntology.ai/paper/2110.14007","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.14007"}},"official":{"repos":["yzhao062/pytod"],"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/learned-robust-pca-a-scalable-deep-unfolding","slug":"learned-robust-pca-a-scalable-deep-unfolding","title":"Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection","date":"2021-10-11","arxiv_id":"2110.05649","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learned-robust-pca-a-scalable-deep-unfolding#ran","syntology_url":"https://syntology.ai/paper/2110.05649","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.05649"}},"official":{"repos":["caesarcai/lrpca"],"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/learn-then-test-calibrating-predictive","slug":"learn-then-test-calibrating-predictive","title":"Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control","date":"2021-10-03","arxiv_id":"2110.01052","repositories_listed":2,"syntology":{"n":22,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":5,"n_honours":3,"n_violates":3,"n_no_contract":11,"n_pointer_only":3,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 3 honoured, 3 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/learn-then-test-calibrating-predictive#ran","syntology_url":"https://syntology.ai/paper/2110.01052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.01052"}},"official":{"repos":["aangelopoulos/ltt"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-total-recall-in-industrial-anomaly","slug":"towards-total-recall-in-industrial-anomaly","title":"Towards Total Recall in Industrial Anomaly Detection","date":"2021-06-15","arxiv_id":"2106.08265","repositories_listed":18,"syntology":{"n":36,"n_ran":28,"n_constructed":0,"n_ran_checked":26,"n_instrument":2,"n_unverified":8,"n_honours":1,"n_violates":1,"n_no_contract":24,"n_pointer_only":4,"phrase":"28 ran (of which 0 constructed an object rather than computing a result; 26 with no instrument failure: 1 honoured, 1 violated, 24 with no contract checked; 2 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/towards-total-recall-in-industrial-anomaly#ran","syntology_url":"https://syntology.ai/paper/2106.08265","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.08265"}},"official":{"repos":["amazon-research/patchcore-inspection"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/deep-clustering-based-fair-outlier-detection","slug":"deep-clustering-based-fair-outlier-detection","title":"Deep Clustering based Fair Outlier Detection","date":"2021-06-09","arxiv_id":"2106.05127","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-clustering-based-fair-outlier-detection#ran","syntology_url":"https://syntology.ai/paper/2106.05127","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05127"}},"official":{"repos":["brandeis-machine-learning/FairOutlierDetection"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/openmatch-open-set-consistency-regularization","slug":"openmatch-open-set-consistency-regularization","title":"OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers","date":"2021-05-28","arxiv_id":"2105.14148","repositories_listed":1,"syntology":{"n":19,"n_ran":15,"n_constructed":0,"n_ran_checked":11,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":10,"n_pointer_only":7,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 1 violated, 10 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/openmatch-open-set-consistency-regularization#ran","syntology_url":"https://syntology.ai/paper/2105.14148","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.14148"}},"official":{"repos":["VisionLearningGroup/OP_Match"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/autoencoding-under-normalization-constraints","slug":"autoencoding-under-normalization-constraints","title":"Autoencoding Under Normalization Constraints","date":"2021-05-12","arxiv_id":"2105.05735","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":4,"n_ran_checked":4,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"7 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/autoencoding-under-normalization-constraints#ran","syntology_url":"https://syntology.ai/paper/2105.05735","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.05735"}},"official":{"repos":["swyoon/normalized-autoencoders"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/ssd-a-unified-framework-for-self-supervised-1","slug":"ssd-a-unified-framework-for-self-supervised-1","title":"SSD: A Unified Framework for Self-Supervised Outlier Detection","date":"2021-03-22","arxiv_id":"2103.12051","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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/ssd-a-unified-framework-for-self-supervised-1#ran","syntology_url":"https://syntology.ai/paper/2103.12051","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12051"}},"official":{"repos":["inspire-group/SSD"],"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/on-using-classification-datasets-to-evaluate","slug":"on-using-classification-datasets-to-evaluate","title":"On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insights","date":"2020-12-23","arxiv_id":"2012.12931","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/on-using-classification-datasets-to-evaluate#ran","syntology_url":"https://syntology.ai/paper/2012.12931","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.12931"}},"official":{"repos":["LingxiaoShawn/GLOD-Issues"],"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/further-analysis-of-outlier-detection-with","slug":"further-analysis-of-outlier-detection-with","title":"Further Analysis of Outlier Detection with Deep Generative Models","date":"2020-10-25","arxiv_id":"2010.13064","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":1,"n_instrument":9,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":11,"phrase":"10 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; 9 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/further-analysis-of-outlier-detection-with#ran","syntology_url":"https://syntology.ai/paper/2010.13064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13064"}},"official":{"repos":["thu-ml/ood-dgm"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-outlier-arm-identification-1","slug":"robust-outlier-arm-identification-1","title":"Robust Outlier Arm Identification","date":"2020-09-21","arxiv_id":"2009.09988","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":1,"n_ran_checked":3,"n_instrument":4,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":7,"phrase":"7 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robust-outlier-arm-identification-1#ran","syntology_url":"https://syntology.ai/paper/2009.09988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.09988"}},"official":{"repos":["yinglunz/ROAI_ICML2020"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-scene-agnostic-framework-with-adversarial","slug":"a-scene-agnostic-framework-with-adversarial","title":"A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video","date":"2020-08-27","arxiv_id":"2008.12328","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-scene-agnostic-framework-with-adversarial#ran","syntology_url":"https://syntology.ai/paper/2008.12328","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.12328"}},"official":{"repos":["lilygeorgescu/AED"],"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/explainable-deep-one-class-classification","slug":"explainable-deep-one-class-classification","title":"Explainable Deep One-Class Classification","date":"2020-07-03","arxiv_id":"2007.01760","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/explainable-deep-one-class-classification#ran","syntology_url":"https://syntology.ai/paper/2007.01760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01760"}},"official":{"repos":["liznerski/fcdd"],"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/variational-autoencoders-for-anomalous-jet","slug":"variational-autoencoders-for-anomalous-jet","title":"Variational Autoencoders for Anomalous Jet Tagging","date":"2020-07-03","arxiv_id":"2007.01850","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/variational-autoencoders-for-anomalous-jet#ran","syntology_url":"https://syntology.ai/paper/2007.01850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01850"}},"official":{"repos":["taolicheng/VAE-Jet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/non-negative-bregman-divergence-minimization","slug":"non-negative-bregman-divergence-minimization","title":"Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation","date":"2020-06-12","arxiv_id":"2006.06979","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":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) · 1 unverified","sample_list":"/paper/non-negative-bregman-divergence-minimization#ran","syntology_url":"https://syntology.ai/paper/2006.06979","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06979"}},"official":{"repos":["MasaKat0/D3RE"],"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/probabilistic-auto-encoder","slug":"probabilistic-auto-encoder","title":"Probabilistic Autoencoder","date":"2020-06-09","arxiv_id":"2006.05479","repositories_listed":4,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"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) · 6 unverified","sample_list":"/paper/probabilistic-auto-encoder#ran","syntology_url":"https://syntology.ai/paper/2006.05479","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05479"}},"official":{"repos":["VMBoehm/PAE","vmboehm/pae-ablation"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/picket-self-supervised-data-diagnostics-for","slug":"picket-self-supervised-data-diagnostics-for","title":"Picket: Guarding Against Corrupted Data in Tabular Data during Learning and Inference","date":"2020-06-08","arxiv_id":"2006.04730","repositories_listed":1,"syntology":{"n":17,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/picket-self-supervised-data-diagnostics-for#ran","syntology_url":"https://syntology.ai/paper/2006.04730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.04730"}},"official":null}},{"url":"/paper/adalam-revisiting-handcrafted-outlier","slug":"adalam-revisiting-handcrafted-outlier","title":"AdaLAM: Revisiting Handcrafted Outlier Detection","date":"2020-06-07","arxiv_id":"2006.04250","repositories_listed":3,"syntology":{"n":19,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/adalam-revisiting-handcrafted-outlier#ran","syntology_url":"https://syntology.ai/paper/2006.04250","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.04250"}},"official":null}},{"url":"/paper/contextual-outlier-detection-in-continuous","slug":"contextual-outlier-detection-in-continuous","title":"Event Outlier Detection in Continuous Time","date":"2019-12-19","arxiv_id":"1912.09522","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/contextual-outlier-detection-in-continuous#ran","syntology_url":"https://syntology.ai/paper/1912.09522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.09522"}},"official":{"repos":["siqil/CPPOD"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mimic-extract-a-data-extraction-preprocessing","slug":"mimic-extract-a-data-extraction-preprocessing","title":"MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III","date":"2019-07-19","arxiv_id":"1907.08322","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mimic-extract-a-data-extraction-preprocessing#ran","syntology_url":"https://syntology.ai/paper/1907.08322","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.08322"}},"official":{"repos":["MLforHealth/MIMIC_Extract"],"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/robust-variational-autoencoders-for-outlier","slug":"robust-variational-autoencoders-for-outlier","title":"Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data","date":"2019-07-15","arxiv_id":"1907.06671","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/robust-variational-autoencoders-for-outlier#ran","syntology_url":"https://syntology.ai/paper/1907.06671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.06671"}},"official":{"repos":["sfme/RVAE_MixedTypes"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/using-self-supervised-learning-can-improve","slug":"using-self-supervised-learning-can-improve","title":"Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty","date":"2019-06-28","arxiv_id":"1906.12340","repositories_listed":4,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":4,"phrase":"11 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/using-self-supervised-learning-can-improve#ran","syntology_url":"https://syntology.ai/paper/1906.12340","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.12340"}},"official":{"repos":["hendrycks/ss-ood"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-semi-supervised-anomaly-detection","slug":"deep-semi-supervised-anomaly-detection","title":"Deep Semi-Supervised Anomaly Detection","date":"2019-06-06","arxiv_id":"1906.02694","repositories_listed":7,"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/deep-semi-supervised-anomaly-detection#ran","syntology_url":"https://syntology.ai/paper/1906.02694","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.02694"}},"official":{"repos":["lukasruff/Deep-SAD-PyTorch"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/190600547","slug":"190600547","title":"MaxGap Bandit: Adaptive Algorithms for Approximate Ranking","date":"2019-06-03","arxiv_id":"1906.00547","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":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) · 2 unverified","sample_list":"/paper/190600547#ran","syntology_url":"https://syntology.ai/paper/1906.00547","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.00547"}},"official":{"repos":["sumeetsk/maxgap_bandit"],"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/elki-a-large-open-source-library-for-data","slug":"elki-a-large-open-source-library-for-data","title":"ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 \"Heidelberg\"","date":"2019-02-10","arxiv_id":"1902.03616","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/elki-a-large-open-source-library-for-data#ran","syntology_url":"https://syntology.ai/paper/1902.03616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.03616"}},"official":{"repos":["elki-project/elki"],"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/pyod-a-python-toolbox-for-scalable-outlier","slug":"pyod-a-python-toolbox-for-scalable-outlier","title":"PyOD: A Python Toolbox for Scalable Outlier Detection","date":"2019-01-06","arxiv_id":"1901.01588","repositories_listed":4,"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/pyod-a-python-toolbox-for-scalable-outlier#ran","syntology_url":"https://syntology.ai/paper/1901.01588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.01588"}},"official":{"repos":["yzhao062/pyod"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/object-centric-auto-encoders-and-dummy","slug":"object-centric-auto-encoders-and-dummy","title":"Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video","date":"2018-12-11","arxiv_id":"1812.04960","repositories_listed":1,"syntology":{"n":21,"n_ran":16,"n_constructed":0,"n_ran_checked":16,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/object-centric-auto-encoders-and-dummy#ran","syntology_url":"https://syntology.ai/paper/1812.04960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.04960"}},"official":null}},{"url":"/paper/morpho-mnist-quantitative-assessment-and","slug":"morpho-mnist-quantitative-assessment-and","title":"Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning","date":"2018-09-27","arxiv_id":"1809.10780","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/morpho-mnist-quantitative-assessment-and#ran","syntology_url":"https://syntology.ai/paper/1809.10780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.10780"}},"official":{"repos":["dccastro/Morpho-MNIST"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-representations-of-ultrahigh","slug":"learning-representations-of-ultrahigh","title":"Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection","date":"2018-06-13","arxiv_id":"1806.04808","repositories_listed":3,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-representations-of-ultrahigh#ran","syntology_url":"https://syntology.ai/paper/1806.04808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.04808"}},"official":null}},{"url":"/paper/lstm-fully-convolutional-networks-for-time","slug":"lstm-fully-convolutional-networks-for-time","title":"LSTM Fully Convolutional Networks for Time Series Classification","date":"2017-09-08","arxiv_id":"1709.05206","repositories_listed":9,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lstm-fully-convolutional-networks-for-time#ran","syntology_url":"https://syntology.ai/paper/1709.05206","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.05206"}},"official":{"repos":["titu1994/LSTM-FCN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/deep-sets","slug":"deep-sets","title":"Deep Sets","date":"2017-03-10","arxiv_id":"1703.06114","repositories_listed":7,"syntology":{"n":12,"n_ran":11,"n_constructed":4,"n_ran_checked":8,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"11 ran (of which 4 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-sets#ran","syntology_url":"https://syntology.ai/paper/1703.06114","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.06114"}},"official":null}},{"url":"/paper/lstm-based-encoder-decoder-for-multi-sensor","slug":"lstm-based-encoder-decoder-for-multi-sensor","title":"LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection","date":"2016-07-01","arxiv_id":"1607.00148","repositories_listed":8,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/lstm-based-encoder-decoder-for-multi-sensor#ran","syntology_url":"https://syntology.ai/paper/1607.00148","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.00148"}},"official":null}}],"record_sha256":"d88e302c3b727b94c8dd9a4fffe1f13758da5549c1191759e13d31d856b814d7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}