{"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/few-shot-learning/papers/ran/3","list_of":"/task/few-shot-learning","task":"Few-Shot Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":3,"pages_in_order":4,"rows_per_page":100,"rows":[201,300],"of":373,"counts":{"archive_papers_tagged":2964,"with_a_code_link":1297,"where_syntology_ran_a_sample":373,"not_listed_spam_title":0,"listed":2964,"listed_where_code_ran":373,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":306,"every_run_a_failure_of_syntologys_instrument":67,"listed_with_a_run_with_no_instrument_failure":306,"listed_every_run_a_failure_of_syntologys_instrument":67,"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/few-shot-learning/papers/ran/1","prev":"/task/few-shot-learning/papers/ran/2","next":"/task/few-shot-learning/papers/ran/4","papers":[{"url":"/paper/sega-semantic-guided-attention-on-visual","slug":"sega-semantic-guided-attention-on-visual","title":"SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning","date":"2021-11-08","arxiv_id":"2111.04316","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"6 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sega-semantic-guided-attention-on-visual#ran","syntology_url":"https://syntology.ai/paper/2111.04316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.04316"}},"official":{"repos":["martayang/sega"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/clues-few-shot-learning-evaluation-in-natural","slug":"clues-few-shot-learning-evaluation-in-natural","title":"CLUES: Few-Shot Learning Evaluation in Natural Language Understanding","date":"2021-11-04","arxiv_id":"2111.02570","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/clues-few-shot-learning-evaluation-in-natural#ran","syntology_url":"https://syntology.ai/paper/2111.02570","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.02570"}},"official":{"repos":["microsoft/clues"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-explanation-of-in-context-learning-as-1","slug":"an-explanation-of-in-context-learning-as-1","title":"An Explanation of In-context Learning as Implicit Bayesian Inference","date":"2021-11-03","arxiv_id":"2111.02080","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-explanation-of-in-context-learning-as-1#ran","syntology_url":"https://syntology.ai/paper/2111.02080","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.02080"}},"official":{"repos":["p-lambda/incontext-learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/metaicl-learning-to-learn-in-context","slug":"metaicl-learning-to-learn-in-context","title":"MetaICL: Learning to Learn In Context","date":"2021-10-29","arxiv_id":"2110.15943","repositories_listed":2,"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/metaicl-learning-to-learn-in-context#ran","syntology_url":"https://syntology.ai/paper/2110.15943","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.15943"}},"official":{"repos":["facebookresearch/metaicl"],"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/neural-view-synthesis-and-matching-for-semi","slug":"neural-view-synthesis-and-matching-for-semi","title":"Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose","date":"2021-10-27","arxiv_id":"2110.14213","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-view-synthesis-and-matching-for-semi#ran","syntology_url":"https://syntology.ai/paper/2110.14213","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.14213"}},"official":{"repos":["angtian/neuralvs"],"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/non-gaussian-gaussian-processes-for-few-shot","slug":"non-gaussian-gaussian-processes-for-few-shot","title":"Non-Gaussian Gaussian Processes for Few-Shot Regression","date":"2021-10-26","arxiv_id":"2110.13561","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/non-gaussian-gaussian-processes-for-few-shot#ran","syntology_url":"https://syntology.ai/paper/2110.13561","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.13561"}},"official":{"repos":["gmum/non-gaussian-gaussian-processes"],"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/hrkd-hierarchical-relational-knowledge","slug":"hrkd-hierarchical-relational-knowledge","title":"HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression","date":"2021-10-16","arxiv_id":"2110.08551","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"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 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/hrkd-hierarchical-relational-knowledge#ran","syntology_url":"https://syntology.ai/paper/2110.08551","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.08551"}},"official":{"repos":["cheneydon/hrkd"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-bot-prompt-based-learning-for","slug":"few-shot-bot-prompt-based-learning-for","title":"Few-Shot Bot: Prompt-Based Learning for Dialogue Systems","date":"2021-10-15","arxiv_id":"2110.08118","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":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/few-shot-bot-prompt-based-learning-for#ran","syntology_url":"https://syntology.ai/paper/2110.08118","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.08118"}},"official":{"repos":["andreamad8/FSB","tunib-ai/parallelformers"],"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/lfpt5-a-unified-framework-for-lifelong-few-1","slug":"lfpt5-a-unified-framework-for-lifelong-few-1","title":"LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5","date":"2021-10-14","arxiv_id":"2110.07298","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/lfpt5-a-unified-framework-for-lifelong-few-1#ran","syntology_url":"https://syntology.ai/paper/2110.07298","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.07298"}},"official":{"repos":["qcwthu/lifelong-fewshot-language-learning"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/can-explanations-be-useful-for-calibrating","slug":"can-explanations-be-useful-for-calibrating","title":"Can Explanations Be Useful for Calibrating Black Box Models?","date":"2021-10-14","arxiv_id":"2110.07586","repositories_listed":2,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/can-explanations-be-useful-for-calibrating#ran","syntology_url":"https://syntology.ai/paper/2110.07586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.07586"}},"official":{"repos":["xiye17/interpcalib"],"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/investigating-the-effect-of-natural-language","slug":"investigating-the-effect-of-natural-language","title":"Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI","date":"2021-10-12","arxiv_id":"2110.06223","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/investigating-the-effect-of-natural-language#ran","syntology_url":"https://syntology.ai/paper/2110.06223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.06223"}},"official":{"repos":["chicagohai/hans-explanations"],"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/list-lite-self-training-makes-efficient-few-1","slug":"list-lite-self-training-makes-efficient-few-1","title":"LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners","date":"2021-10-12","arxiv_id":"2110.06274","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":4,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/list-lite-self-training-makes-efficient-few-1#ran","syntology_url":"https://syntology.ai/paper/2110.06274","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.06274"}},"official":{"repos":["microsoft/list"],"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/meta-learning-with-task-adaptive-loss-1","slug":"meta-learning-with-task-adaptive-loss-1","title":"Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning","date":"2021-10-08","arxiv_id":"2110.03909","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/meta-learning-with-task-adaptive-loss-1#ran","syntology_url":"https://syntology.ai/paper/2110.03909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.03909"}},"official":{"repos":["baiksung/MeTAL"],"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/sparse-moes-meet-efficient-ensembles","slug":"sparse-moes-meet-efficient-ensembles","title":"Sparse MoEs meet Efficient Ensembles","date":"2021-10-07","arxiv_id":"2110.03360","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/sparse-moes-meet-efficient-ensembles#ran","syntology_url":"https://syntology.ai/paper/2110.03360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.03360"}},"official":{"repos":["google-research/vmoe"],"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/revisiting-self-training-for-few-shot","slug":"revisiting-self-training-for-few-shot","title":"Revisiting Self-Training for Few-Shot Learning of Language Model","date":"2021-10-04","arxiv_id":"2110.01256","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/revisiting-self-training-for-few-shot#ran","syntology_url":"https://syntology.ai/paper/2110.01256","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.01256"}},"official":{"repos":["matthewcym/sflm"],"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":["found_in_text","official"]}}},{"url":"/paper/generalization-bounds-for-meta-learning-an","slug":"generalization-bounds-for-meta-learning-an","title":"Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis","date":"2021-09-29","arxiv_id":"2109.14595","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/generalization-bounds-for-meta-learning-an#ran","syntology_url":"https://syntology.ai/paper/2109.14595","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.14595"}},"official":{"repos":["livreq/meta-sgld"],"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/sparse-spatial-transformers-for-few-shot","slug":"sparse-spatial-transformers-for-few-shot","title":"Sparse Spatial Transformers for Few-Shot Learning","date":"2021-09-27","arxiv_id":"2109.12932","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/sparse-spatial-transformers-for-few-shot#ran","syntology_url":"https://syntology.ai/paper/2109.12932","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.12932"}},"official":{"repos":["chenhaoxing/ssformers"],"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/online-unsupervised-learning-of-visual","slug":"online-unsupervised-learning-of-visual","title":"Online Unsupervised Learning of Visual Representations and Categories","date":"2021-09-13","arxiv_id":"2109.05675","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/online-unsupervised-learning-of-visual#ran","syntology_url":"https://syntology.ai/paper/2109.05675","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.05675"}},"official":{"repos":["renmengye/online-unsup-proto-net"],"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/what-changes-can-large-scale-language-models","slug":"what-changes-can-large-scale-language-models","title":"What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers","date":"2021-09-10","arxiv_id":"2109.04650","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/what-changes-can-large-scale-language-models#ran","syntology_url":"https://syntology.ai/paper/2109.04650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04650"}},"official":null}},{"url":"/paper/libfewshot-a-comprehensive-library-for-few","slug":"libfewshot-a-comprehensive-library-for-few","title":"LibFewShot: A Comprehensive Library for Few-shot Learning","date":"2021-09-10","arxiv_id":"2109.04898","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/libfewshot-a-comprehensive-library-for-few#ran","syntology_url":"https://syntology.ai/paper/2109.04898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04898"}},"official":{"repos":["rl-vig/libfewshot"],"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/ppt-pre-trained-prompt-tuning-for-few-shot","slug":"ppt-pre-trained-prompt-tuning-for-few-shot","title":"PPT: Pre-trained Prompt Tuning for Few-shot Learning","date":"2021-09-09","arxiv_id":"2109.04332","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ppt-pre-trained-prompt-tuning-for-few-shot#ran","syntology_url":"https://syntology.ai/paper/2109.04332","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04332"}},"official":{"repos":["thu-coai/ppt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/discrete-and-soft-prompting-for-multilingual","slug":"discrete-and-soft-prompting-for-multilingual","title":"Discrete and Soft Prompting for Multilingual Models","date":"2021-09-08","arxiv_id":"2109.03630","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":3,"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, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/discrete-and-soft-prompting-for-multilingual#ran","syntology_url":"https://syntology.ai/paper/2109.03630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.03630"}},"official":{"repos":["mprompting/xlmrprompt"],"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":["official"]}}},{"url":"/paper/nearest-neighbour-few-shot-learning-for-cross","slug":"nearest-neighbour-few-shot-learning-for-cross","title":"Nearest Neighbour Few-Shot Learning for Cross-lingual Classification","date":"2021-09-06","arxiv_id":"2109.02221","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":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/nearest-neighbour-few-shot-learning-for-cross#ran","syntology_url":"https://syntology.ai/paper/2109.02221","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.02221"}},"official":null}},{"url":"/paper/robust-retrieval-augmented-generation-for","slug":"robust-retrieval-augmented-generation-for","title":"Robust Retrieval Augmented Generation for Zero-shot Slot Filling","date":"2021-08-31","arxiv_id":"2108.13934","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/robust-retrieval-augmented-generation-for#ran","syntology_url":"https://syntology.ai/paper/2108.13934","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.13934"}},"official":{"repos":["ibm/kgi-slot-filling"],"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/counterfactual-attention-learning-for-fine","slug":"counterfactual-attention-learning-for-fine","title":"Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification","date":"2021-08-19","arxiv_id":"2108.08728","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":7,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 7 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) · 3 unverified","sample_list":"/paper/counterfactual-attention-learning-for-fine#ran","syntology_url":"https://syntology.ai/paper/2108.08728","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.08728"}},"official":{"repos":["raoyongming/CAL"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":7,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/generalized-and-incremental-few-shot-learning","slug":"generalized-and-incremental-few-shot-learning","title":"Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting","date":"2021-08-18","arxiv_id":"2108.08165","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":3,"n_ran_checked":7,"n_instrument":3,"n_unverified":3,"n_honours":3,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"10 ran (of which 3 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/generalized-and-incremental-few-shot-learning#ran","syntology_url":"https://syntology.ai/paper/2108.08165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.08165"}},"official":{"repos":["annusha/lcwof"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":3,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/flipda-effective-and-robust-data-augmentation","slug":"flipda-effective-and-robust-data-augmentation","title":"FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning","date":"2021-08-13","arxiv_id":"2108.06332","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/flipda-effective-and-robust-data-augmentation#ran","syntology_url":"https://syntology.ai/paper/2108.06332","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.06332"}},"official":{"repos":["zhouj8553/flipda"],"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/boosting-the-generalization-capability-in","slug":"boosting-the-generalization-capability-in","title":"Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder","date":"2021-08-11","arxiv_id":"2108.05028","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/boosting-the-generalization-capability-in#ran","syntology_url":"https://syntology.ai/paper/2108.05028","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05028"}},"official":null}},{"url":"/paper/deep-metric-learning-for-open-world-semantic","slug":"deep-metric-learning-for-open-world-semantic","title":"Deep Metric Learning for Open World Semantic Segmentation","date":"2021-08-10","arxiv_id":"2108.04562","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"5 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; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/deep-metric-learning-for-open-world-semantic#ran","syntology_url":"https://syntology.ai/paper/2108.04562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.04562"}},"official":null}},{"url":"/paper/transductive-few-shot-classification-on-the","slug":"transductive-few-shot-classification-on-the","title":"Transductive Few-Shot Classification on the Oblique Manifold","date":"2021-08-09","arxiv_id":"2108.04009","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":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/transductive-few-shot-classification-on-the#ran","syntology_url":"https://syntology.ai/paper/2108.04009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.04009"}},"official":{"repos":["GuodongQi/FSL-OM"],"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/noisy-channel-language-model-prompting-for","slug":"noisy-channel-language-model-prompting-for","title":"Noisy Channel Language Model Prompting for Few-Shot Text Classification","date":"2021-08-09","arxiv_id":"2108.04106","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/noisy-channel-language-model-prompting-for#ran","syntology_url":"https://syntology.ai/paper/2108.04106","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.04106"}},"official":{"repos":["shmsw25/Channel-LM-Prompting"],"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/elaborative-rehearsal-for-zero-shot-action","slug":"elaborative-rehearsal-for-zero-shot-action","title":"Elaborative Rehearsal for Zero-shot Action Recognition","date":"2021-08-05","arxiv_id":"2108.02833","repositories_listed":1,"syntology":{"n":15,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/elaborative-rehearsal-for-zero-shot-action#ran","syntology_url":"https://syntology.ai/paper/2108.02833","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.02833"}},"official":{"repos":["DeLightCMU/ElaborativeRehearsal"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/uniform-sampling-over-episode-difficulty","slug":"uniform-sampling-over-episode-difficulty","title":"Uniform Sampling over Episode Difficulty","date":"2021-08-03","arxiv_id":"2108.01662","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/uniform-sampling-over-episode-difficulty#ran","syntology_url":"https://syntology.ai/paper/2108.01662","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.01662"}},"official":{"repos":["amazon-science/uniform-episodic-sampling"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"url":"/paper/recurrent-mask-refinement-for-few-shot","slug":"recurrent-mask-refinement-for-few-shot","title":"Recurrent Mask Refinement for Few-Shot Medical Image Segmentation","date":"2021-08-02","arxiv_id":"2108.00622","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":4,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":9,"phrase":"7 ran (of which 4 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/recurrent-mask-refinement-for-few-shot#ran","syntology_url":"https://syntology.ai/paper/2108.00622","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00622"}},"official":{"repos":["uci-cbcl/RP-Net"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":4,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/from-lsat-the-progress-and-challenges-of","slug":"from-lsat-the-progress-and-challenges-of","title":"From LSAT: The Progress and Challenges of Complex Reasoning","date":"2021-08-02","arxiv_id":"2108.00648","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"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) · 4 unverified","sample_list":"/paper/from-lsat-the-progress-and-challenges-of#ran","syntology_url":"https://syntology.ai/paper/2108.00648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00648"}},"official":null}},{"url":"/paper/prototransformer-a-meta-learning-approach-to","slug":"prototransformer-a-meta-learning-approach-to","title":"ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback","date":"2021-07-23","arxiv_id":"2107.14035","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":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) · 0 unverified","sample_list":"/paper/prototransformer-a-meta-learning-approach-to#ran","syntology_url":"https://syntology.ai/paper/2107.14035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.14035"}},"official":null}},{"url":"/paper/few-shots-is-all-you-need-a-progressive-few","slug":"few-shots-is-all-you-need-a-progressive-few","title":"Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition","date":"2021-07-21","arxiv_id":"2107.10064","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/few-shots-is-all-you-need-a-progressive-few#ran","syntology_url":"https://syntology.ai/paper/2107.10064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.10064"}},"official":{"repos":["dali92002/htrbymatching"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/flex-unifying-evaluation-for-few-shot-nlp","slug":"flex-unifying-evaluation-for-few-shot-nlp","title":"FLEX: Unifying Evaluation for Few-Shot NLP","date":"2021-07-15","arxiv_id":"2107.07170","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/flex-unifying-evaluation-for-few-shot-nlp#ran","syntology_url":"https://syntology.ai/paper/2107.07170","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.07170"}},"official":{"repos":["allenai/flex","allenai/unifew"],"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/maml-is-a-noisy-contrastive-learner","slug":"maml-is-a-noisy-contrastive-learner","title":"MAML is a Noisy Contrastive Learner in Classification","date":"2021-06-29","arxiv_id":"2106.15367","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/maml-is-a-noisy-contrastive-learner#ran","syntology_url":"https://syntology.ai/paper/2106.15367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.15367"}},"official":null}},{"url":"/paper/cutting-down-on-prompts-and-parameters-simple","slug":"cutting-down-on-prompts-and-parameters-simple","title":"Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models","date":"2021-06-24","arxiv_id":"2106.13353","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/cutting-down-on-prompts-and-parameters-simple#ran","syntology_url":"https://syntology.ai/paper/2106.13353","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.13353"}},"official":{"repos":["ucinlp/null-prompts"],"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/unsupervised-embedding-adaptation-via-early","slug":"unsupervised-embedding-adaptation-via-early","title":"Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification","date":"2021-06-22","arxiv_id":"2106.11486","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/unsupervised-embedding-adaptation-via-early#ran","syntology_url":"https://syntology.ai/paper/2106.11486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11486"}},"official":{"repos":["movinghoon/ESFR"],"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/long-term-cross-adversarial-training-a-robust","slug":"long-term-cross-adversarial-training-a-robust","title":"Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks","date":"2021-06-22","arxiv_id":"2106.12900","repositories_listed":1,"syntology":{"n":9,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":8,"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) · 8 unverified","sample_list":"/paper/long-term-cross-adversarial-training-a-robust#ran","syntology_url":"https://syntology.ai/paper/2106.12900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.12900"}},"official":{"repos":["Gnomeek/Long-term-Cross-Adversarial-Training"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/evograd-efficient-gradient-based-meta","slug":"evograd-efficient-gradient-based-meta","title":"EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization","date":"2021-06-19","arxiv_id":"2106.10575","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 3 unverified","sample_list":"/paper/evograd-efficient-gradient-based-meta#ran","syntology_url":"https://syntology.ai/paper/2106.10575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10575"}},"official":{"repos":["ondrejbohdal/evograd"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["community","official"]}}},{"url":"/paper/generate-annotate-and-learn-generative-models","slug":"generate-annotate-and-learn-generative-models","title":"Generate, Annotate, and Learn: NLP with Synthetic Text","date":"2021-06-11","arxiv_id":"2106.06168","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/generate-annotate-and-learn-generative-models#ran","syntology_url":"https://syntology.ai/paper/2106.06168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06168"}},"official":{"repos":["xlhex/gal_syntex"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/attentional-meta-learners-are-polythetic","slug":"attentional-meta-learners-are-polythetic","title":"Attentional Meta-learners for Few-shot Polythetic Classification","date":"2021-06-09","arxiv_id":"2106.05317","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/attentional-meta-learners-are-polythetic#ran","syntology_url":"https://syntology.ai/paper/2106.05317","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05317"}},"official":{"repos":["rvinas/polythetic_metalearning"],"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/detreg-unsupervised-pretraining-with-region","slug":"detreg-unsupervised-pretraining-with-region","title":"DETReg: Unsupervised Pretraining with Region Priors for Object Detection","date":"2021-06-08","arxiv_id":"2106.04550","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":1,"phrase":"5 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/detreg-unsupervised-pretraining-with-region#ran","syntology_url":"https://syntology.ai/paper/2106.04550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04550"}},"official":{"repos":["amirbar/detreg"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/reordering-examples-helps-during-priming","slug":"reordering-examples-helps-during-priming","title":"Reordering Examples Helps during Priming-based Few-Shot Learning","date":"2021-06-03","arxiv_id":"2106.01751","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"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) · 4 unverified","sample_list":"/paper/reordering-examples-helps-during-priming#ran","syntology_url":"https://syntology.ai/paper/2106.01751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01751"}},"official":{"repos":["SawanKumar28/pero"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/ethical-advice-taker-do-language-models","slug":"ethical-advice-taker-do-language-models","title":"Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?","date":"2021-06-02","arxiv_id":"2106.01465","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ethical-advice-taker-do-language-models#ran","syntology_url":"https://syntology.ai/paper/2106.01465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01465"}},"official":{"repos":["allenai/ethical-interventions"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-the-similarity-of-representations","slug":"exploring-the-similarity-of-representations","title":"Exploring the Similarity of Representations in Model-Agnostic Meta-Learning","date":"2021-05-12","arxiv_id":"2105.05757","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":3,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/exploring-the-similarity-of-representations#ran","syntology_url":"https://syntology.ai/paper/2105.05757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.05757"}},"official":{"repos":["ThomasGoerttler/similarity-analysis-of-maml"],"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/representation-learning-via-global-temporal","slug":"representation-learning-via-global-temporal","title":"Representation Learning via Global Temporal Alignment and Cycle-Consistency","date":"2021-05-11","arxiv_id":"2105.05217","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"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) · 2 unverified","sample_list":"/paper/representation-learning-via-global-temporal#ran","syntology_url":"https://syntology.ai/paper/2105.05217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.05217"}},"official":{"repos":["hadjisma/VideoAlignment"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/entailment-as-few-shot-learner","slug":"entailment-as-few-shot-learner","title":"Entailment as Few-Shot Learner","date":"2021-04-29","arxiv_id":"2104.14690","repositories_listed":3,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/entailment-as-few-shot-learner#ran","syntology_url":"https://syntology.ai/paper/2104.14690","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.14690"}},"official":null}},{"url":"/paper/uvstyle-net-unsupervised-few-shot-learning-of","slug":"uvstyle-net-unsupervised-few-shot-learning-of","title":"UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps","date":"2021-04-28","arxiv_id":"2105.02961","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/uvstyle-net-unsupervised-few-shot-learning-of#ran","syntology_url":"https://syntology.ai/paper/2105.02961","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.02961"}},"official":{"repos":["AutodeskAILab/UVStyle-Net"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-power-of-scale-for-parameter-efficient","slug":"the-power-of-scale-for-parameter-efficient","title":"The Power of Scale for Parameter-Efficient Prompt Tuning","date":"2021-04-18","arxiv_id":"2104.08691","repositories_listed":12,"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/the-power-of-scale-for-parameter-efficient#ran","syntology_url":"https://syntology.ai/paper/2104.08691","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.08691"}},"official":{"repos":["google-research/prompt-tuning"],"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/does-language-help-generalization-in-vision","slug":"does-language-help-generalization-in-vision","title":"Does language help generalization in vision models?","date":"2021-04-16","arxiv_id":"2104.08313","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/does-language-help-generalization-in-vision#ran","syntology_url":"https://syntology.ai/paper/2104.08313","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.08313"}},"official":{"repos":["bdvllrs/generalization-vision"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bert-memorisation-and-pitfalls-in-low","slug":"bert-memorisation-and-pitfalls-in-low","title":"Memorisation versus Generalisation in Pre-trained Language Models","date":"2021-04-16","arxiv_id":"2105.00828","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/bert-memorisation-and-pitfalls-in-low#ran","syntology_url":"https://syntology.ai/paper/2105.00828","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.00828"}},"official":{"repos":["Michael-Tanzer/BERT-mem-lowres"],"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/how-sensitive-are-meta-learners-to-dataset","slug":"how-sensitive-are-meta-learners-to-dataset","title":"How Sensitive are Meta-Learners to Dataset Imbalance?","date":"2021-04-12","arxiv_id":"2104.05344","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":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/how-sensitive-are-meta-learners-to-dataset#ran","syntology_url":"https://syntology.ai/paper/2104.05344","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.05344"}},"official":{"repos":["mattochal/imbalanced_fsl_public"],"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/support-target-protocol-for-meta-learning","slug":"support-target-protocol-for-meta-learning","title":"Towards Enabling Meta-Learning from Target Models","date":"2021-04-08","arxiv_id":"2104.03736","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/support-target-protocol-for-meta-learning#ran","syntology_url":"https://syntology.ai/paper/2104.03736","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.03736"}},"official":{"repos":["njulus/ST"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/orbit-a-real-world-few-shot-dataset-for","slug":"orbit-a-real-world-few-shot-dataset-for","title":"ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition","date":"2021-04-08","arxiv_id":"2104.03841","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/orbit-a-real-world-few-shot-dataset-for#ran","syntology_url":"https://syntology.ai/paper/2104.03841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.03841"}},"official":{"repos":["microsoft/ORBIT-Dataset"],"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/von-mises-fisher-loss-an-exploration-of","slug":"von-mises-fisher-loss-an-exploration-of","title":"von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning","date":"2021-03-29","arxiv_id":"2103.15718","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/von-mises-fisher-loss-an-exploration-of#ran","syntology_url":"https://syntology.ai/paper/2103.15718","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15718"}},"official":{"repos":["google-research/vmf_embeddings"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-dynamic-alignment-via-meta-filter","slug":"learning-dynamic-alignment-via-meta-filter","title":"Learning Dynamic Alignment via Meta-filter for Few-shot Learning","date":"2021-03-25","arxiv_id":"2103.13582","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":7,"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 7 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; every one of the 7 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learning-dynamic-alignment-via-meta-filter#ran","syntology_url":"https://syntology.ai/paper/2103.13582","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.13582"}},"official":null}},{"url":"/paper/orthogonal-projection-loss","slug":"orthogonal-projection-loss","title":"Orthogonal Projection Loss","date":"2021-03-25","arxiv_id":"2103.14021","repositories_listed":1,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"8 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; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/orthogonal-projection-loss#ran","syntology_url":"https://syntology.ai/paper/2103.14021","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14021"}},"official":{"repos":["kahnchana/opl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/prototypical-representation-learning-for-1","slug":"prototypical-representation-learning-for-1","title":"Prototypical Representation Learning for Relation Extraction","date":"2021-03-22","arxiv_id":"2103.11647","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/prototypical-representation-learning-for-1#ran","syntology_url":"https://syntology.ai/paper/2103.11647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.11647"}},"official":{"repos":["Alibaba-NLP/ProtoRE"],"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/repurposing-pretrained-models-for-robust-out-1","slug":"repurposing-pretrained-models-for-robust-out-1","title":"Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning","date":"2021-03-16","arxiv_id":"2103.09027","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/repurposing-pretrained-models-for-robust-out-1#ran","syntology_url":"https://syntology.ai/paper/2103.09027","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.09027"}},"official":{"repos":["NamyeongK/USA_UFGSM"],"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/fsce-few-shot-object-detection-via","slug":"fsce-few-shot-object-detection-via","title":"FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding","date":"2021-03-10","arxiv_id":"2103.05950","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fsce-few-shot-object-detection-via#ran","syntology_url":"https://syntology.ai/paper/2103.05950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.05950"}},"official":{"repos":["MegviiDetection/FSCE"],"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/few-shot-open-set-recognition-by","slug":"few-shot-open-set-recognition-by","title":"Few-shot Open-set Recognition by Transformation Consistency","date":"2021-03-02","arxiv_id":"2103.01537","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"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 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) · 2 unverified","sample_list":"/paper/few-shot-open-set-recognition-by#ran","syntology_url":"https://syntology.ai/paper/2103.01537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.01537"}},"official":null}},{"url":"/paper/exploring-complementary-strengths-of","slug":"exploring-complementary-strengths-of","title":"Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning","date":"2021-03-01","arxiv_id":"2103.01315","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/exploring-complementary-strengths-of#ran","syntology_url":"https://syntology.ai/paper/2103.01315","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.01315"}},"official":{"repos":["nayeemrizve/invariance-equivariance"],"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/neural-data-augmentation-via-example","slug":"neural-data-augmentation-via-example","title":"Neural Data Augmentation via Example Extrapolation","date":"2021-02-02","arxiv_id":"2102.01335","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/neural-data-augmentation-via-example#ran","syntology_url":"https://syntology.ai/paper/2102.01335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.01335"}},"official":{"repos":["google/example_extrapolation"],"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/similarity-of-classification-tasks","slug":"similarity-of-classification-tasks","title":"Similarity of Classification Tasks","date":"2021-01-27","arxiv_id":"2101.11201","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/similarity-of-classification-tasks#ran","syntology_url":"https://syntology.ai/paper/2101.11201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.11201"}},"official":{"repos":["cnguyen10/similarity_classification_tasks"],"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/what-makes-good-in-context-examples-for-gpt-3","slug":"what-makes-good-in-context-examples-for-gpt-3","title":"What Makes Good In-Context Examples for GPT-$3$?","date":"2021-01-17","arxiv_id":"2101.06804","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/what-makes-good-in-context-examples-for-gpt-3#ran","syntology_url":"https://syntology.ai/paper/2101.06804","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.06804"}},"official":null}},{"url":"/paper/free-lunch-for-few-shot-learning-distribution-1","slug":"free-lunch-for-few-shot-learning-distribution-1","title":"Free Lunch for Few-shot Learning: Distribution Calibration","date":"2021-01-16","arxiv_id":"2101.06395","repositories_listed":6,"syntology":{"n":6,"n_ran":4,"n_constructed":2,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/free-lunch-for-few-shot-learning-distribution-1#ran","syntology_url":"https://syntology.ai/paper/2101.06395","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.06395"}},"official":{"repos":["ShuoYang-1998/Few_Shot_Distribution_Calibration","ShuoYang-1998/ICLR2021-Oral_Distribution_Calibration"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/shallow-bayesian-meta-learning-for-real-world","slug":"shallow-bayesian-meta-learning-for-real-world","title":"Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition","date":"2021-01-08","arxiv_id":"2101.02833","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/shallow-bayesian-meta-learning-for-real-world#ran","syntology_url":"https://syntology.ai/paper/2101.02833","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.02833"}},"official":{"repos":["open-debin/bayesian_mqda"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-learning-with-class-imbalance","slug":"few-shot-learning-with-class-imbalance","title":"Few-Shot Learning with Class Imbalance","date":"2021-01-07","arxiv_id":"2101.02523","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/few-shot-learning-with-class-imbalance#ran","syntology_url":"https://syntology.ai/paper/2101.02523","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.02523"}},"official":{"repos":["mattochal/imbalanced_fsl_public"],"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/making-pre-trained-language-models-better-few","slug":"making-pre-trained-language-models-better-few","title":"Making Pre-trained Language Models Better Few-shot Learners","date":"2020-12-31","arxiv_id":"2012.15723","repositories_listed":9,"syntology":{"n":9,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":7,"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) · 7 unverified","sample_list":"/paper/making-pre-trained-language-models-better-few#ran","syntology_url":"https://syntology.ai/paper/2012.15723","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.15723"}},"official":{"repos":["princeton-nlp/LM-BFF"],"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/feature-learning-in-infinite-width-neural","slug":"feature-learning-in-infinite-width-neural","title":"Feature Learning in Infinite-Width Neural Networks","date":"2020-11-30","arxiv_id":"2011.14522","repositories_listed":4,"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/feature-learning-in-infinite-width-neural#ran","syntology_url":"https://syntology.ai/paper/2011.14522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.14522"}},"official":{"repos":["edwardjhu/TP4"],"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/how-well-do-self-supervised-models-transfer","slug":"how-well-do-self-supervised-models-transfer","title":"How Well Do Self-Supervised Models Transfer?","date":"2020-11-26","arxiv_id":"2011.13377","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/how-well-do-self-supervised-models-transfer#ran","syntology_url":"https://syntology.ai/paper/2011.13377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.13377"}},"official":{"repos":["linusericsson/ssl-transfer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-learning-with-adaptive-hyperparameters","slug":"meta-learning-with-adaptive-hyperparameters","title":"Meta-Learning with Adaptive Hyperparameters","date":"2020-10-31","arxiv_id":"2011.00209","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/meta-learning-with-adaptive-hyperparameters#ran","syntology_url":"https://syntology.ai/paper/2011.00209","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.00209"}},"official":{"repos":["baiksung/ALFA","google-research/meta-dataset"],"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/discriminative-nearest-neighbor-few-shot","slug":"discriminative-nearest-neighbor-few-shot","title":"Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference","date":"2020-10-25","arxiv_id":"2010.13009","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/discriminative-nearest-neighbor-few-shot#ran","syntology_url":"https://syntology.ai/paper/2010.13009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13009"}},"official":{"repos":["salesforce/DNNC-few-shot-intent"],"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/self-training-for-few-shot-transfer-across-1","slug":"self-training-for-few-shot-transfer-across-1","title":"Self-training for Few-shot Transfer Across Extreme Task Differences","date":"2020-10-15","arxiv_id":"2010.07734","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-training-for-few-shot-transfer-across-1#ran","syntology_url":"https://syntology.ai/paper/2010.07734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.07734"}},"official":{"repos":["cpphoo/STARTUP"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-domain-few-shot-learning-by-1","slug":"cross-domain-few-shot-learning-by-1","title":"Cross-Domain Few-Shot Learning by Representation Fusion","date":"2020-10-13","arxiv_id":"2010.06498","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":1,"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; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cross-domain-few-shot-learning-by-1#ran","syntology_url":"https://syntology.ai/paper/2010.06498","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.06498"}},"official":{"repos":["ml-jku/chef","tomte812/chef"],"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/self-supervised-few-shot-learning-on-point","slug":"self-supervised-few-shot-learning-on-point","title":"Self-Supervised Few-Shot Learning on Point Clouds","date":"2020-09-29","arxiv_id":"2009.14168","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":3,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"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) · 1 unverified","sample_list":"/paper/self-supervised-few-shot-learning-on-point#ran","syntology_url":"https://syntology.ai/paper/2009.14168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.14168"}},"official":null}},{"url":"/paper/interventional-few-shot-learning","slug":"interventional-few-shot-learning","title":"Interventional Few-Shot Learning","date":"2020-09-28","arxiv_id":"2009.13000","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/interventional-few-shot-learning#ran","syntology_url":"https://syntology.ai/paper/2009.13000","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.13000"}},"official":{"repos":["yue-zhongqi/ifsl"],"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/few-shot-unsupervised-continual-learning","slug":"few-shot-unsupervised-continual-learning","title":"Few-Shot Unsupervised Continual Learning through Meta-Examples","date":"2020-09-17","arxiv_id":"2009.08107","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":1,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/few-shot-unsupervised-continual-learning#ran","syntology_url":"https://syntology.ai/paper/2009.08107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.08107"}},"official":{"repos":["alessiabertugli/FUSION"],"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":["found_in_text","official"]}}},{"url":"/paper/prototype-completion-with-primitive-knowledge","slug":"prototype-completion-with-primitive-knowledge","title":"Prototype Completion with Primitive Knowledge for Few-Shot Learning","date":"2020-09-10","arxiv_id":"2009.04960","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/prototype-completion-with-primitive-knowledge#ran","syntology_url":"https://syntology.ai/paper/2009.04960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.04960"}},"official":{"repos":["zhangbq-research/Prototype_Completion_for_FSL"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learn2learn-a-library-for-meta-learning","slug":"learn2learn-a-library-for-meta-learning","title":"learn2learn: A Library for Meta-Learning Research","date":"2020-08-27","arxiv_id":"2008.12284","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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/learn2learn-a-library-for-meta-learning#ran","syntology_url":"https://syntology.ai/paper/2008.12284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.12284"}},"official":{"repos":["learnables/learn2learn"],"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/transductive-information-maximization-for-few","slug":"transductive-information-maximization-for-few","title":"Transductive Information Maximization For Few-Shot Learning","date":"2020-08-25","arxiv_id":"2008.11297","repositories_listed":2,"syntology":{"n":10,"n_ran":9,"n_constructed":2,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"9 ran (of which 2 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/transductive-information-maximization-for-few#ran","syntology_url":"https://syntology.ai/paper/2008.11297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.11297"}},"official":{"repos":["mboudiaf/TIM"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/does-maml-really-want-feature-reuse-only","slug":"does-maml-really-want-feature-reuse-only","title":"BOIL: Towards Representation Change for Few-shot Learning","date":"2020-08-20","arxiv_id":"2008.08882","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"1 ran (of which 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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/does-maml-really-want-feature-reuse-only#ran","syntology_url":"https://syntology.ai/paper/2008.08882","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.08882"}},"official":null}},{"url":"/paper/concept-learners-for-generalizable-few-shot","slug":"concept-learners-for-generalizable-few-shot","title":"Concept Learners for Few-Shot Learning","date":"2020-07-14","arxiv_id":"2007.07375","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"1 ran (of which 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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/concept-learners-for-generalizable-few-shot#ran","syntology_url":"https://syntology.ai/paper/2007.07375","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.07375"}},"official":{"repos":["snap-stanford/comet"],"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/wandering-within-a-world-online","slug":"wandering-within-a-world-online","title":"Wandering Within a World: Online Contextualized Few-Shot Learning","date":"2020-07-09","arxiv_id":"2007.04546","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/wandering-within-a-world-online#ran","syntology_url":"https://syntology.ai/paper/2007.04546","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04546"}},"official":{"repos":["renmengye/oc-fewshot-public"],"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/predicting-the-accuracy-of-a-few-shot","slug":"predicting-the-accuracy-of-a-few-shot","title":"Predicting the Accuracy of a Few-Shot Classifier","date":"2020-07-08","arxiv_id":"2007.04238","repositories_listed":1,"syntology":{"n":24,"n_ran":19,"n_constructed":0,"n_ran_checked":19,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":0,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/predicting-the-accuracy-of-a-few-shot#ran","syntology_url":"https://syntology.ai/paper/2007.04238","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04238"}},"official":{"repos":["mbonto/fewshot_generalization"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":0,"n_ran_no_instrument_failure":19,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/laplacian-regularized-few-shot-learning-1","slug":"laplacian-regularized-few-shot-learning-1","title":"Laplacian Regularized Few-Shot Learning","date":"2020-06-28","arxiv_id":"2006.15486","repositories_listed":2,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"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) · 7 unverified","sample_list":"/paper/laplacian-regularized-few-shot-learning-1#ran","syntology_url":"https://syntology.ai/paper/2006.15486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.15486"}},"official":{"repos":["imtiazziko/LaplacianShot"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/global-convergence-and-induced-kernels-of","slug":"global-convergence-and-induced-kernels-of","title":"Global Convergence and Generalization Bound of Gradient-Based Meta-Learning with Deep Neural Nets","date":"2020-06-25","arxiv_id":"2006.14606","repositories_listed":2,"syntology":{"n":22,"n_ran":18,"n_constructed":0,"n_ran_checked":15,"n_instrument":3,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":2,"phrase":"18 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; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/global-convergence-and-induced-kernels-of#ran","syntology_url":"https://syntology.ai/paper/2006.14606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.14606"}},"official":{"repos":["AI-secure/Meta-Neural-Kernel","facebookresearch/higher"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-prototypical-transfer","slug":"self-supervised-prototypical-transfer","title":"Self-Supervised Prototypical Transfer Learning for Few-Shot Classification","date":"2020-06-19","arxiv_id":"2006.11325","repositories_listed":2,"syntology":{"n":9,"n_ran":8,"n_constructed":1,"n_ran_checked":1,"n_instrument":7,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"8 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; 7 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-prototypical-transfer#ran","syntology_url":"https://syntology.ai/paper/2006.11325","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.11325"}},"official":{"repos":["indy-lab/ProtoTransfer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/improving-few-shot-visual-classification-with","slug":"improving-few-shot-visual-classification-with","title":"Enhancing Few-Shot Image Classification with Unlabelled Examples","date":"2020-06-17","arxiv_id":"2006.12245","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"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) · 2 unverified","sample_list":"/paper/improving-few-shot-visual-classification-with#ran","syntology_url":"https://syntology.ai/paper/2006.12245","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12245"}},"official":{"repos":["plai-group/simple-cnaps"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/leveraging-the-feature-distribution-in","slug":"leveraging-the-feature-distribution-in","title":"Leveraging the Feature Distribution in Transfer-based Few-Shot Learning","date":"2020-06-06","arxiv_id":"2006.03806","repositories_listed":6,"syntology":{"n":26,"n_ran":22,"n_constructed":0,"n_ran_checked":13,"n_instrument":9,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":12,"n_pointer_only":12,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 0 violated, 12 with no contract checked; 9 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/leveraging-the-feature-distribution-in#ran","syntology_url":"https://syntology.ai/paper/2006.03806","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.03806"}},"official":{"repos":["yhu01/PT-MAP"],"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":["listed","official"]}}},{"url":"/paper/learning-from-context-agnostic-synthetic-data","slug":"learning-from-context-agnostic-synthetic-data","title":"Towards Context-Agnostic Learning Using Synthetic Data","date":"2020-05-29","arxiv_id":"2005.14707","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learning-from-context-agnostic-synthetic-data#ran","syntology_url":"https://syntology.ai/paper/2005.14707","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.14707"}},"official":{"repos":["charlesjin/synthetic_data"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/language-models-are-few-shot-learners","slug":"language-models-are-few-shot-learners","title":"Language Models are Few-Shot Learners","date":"2020-05-28","arxiv_id":"2005.14165","repositories_listed":67,"syntology":{"n":65,"n_ran":45,"n_constructed":0,"n_ran_checked":40,"n_instrument":5,"n_unverified":20,"n_honours":2,"n_violates":1,"n_no_contract":37,"n_pointer_only":7,"phrase":"45 ran (of which 0 constructed an object rather than computing a result; 40 with no instrument failure: 2 honoured, 1 violated, 37 with no contract checked; 5 where Syntology's instrument failed) · 20 unverified","sample_list":"/paper/language-models-are-few-shot-learners#ran","syntology_url":"https://syntology.ai/paper/2005.14165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.14165"}},"official":{"repos":["openai/gpt-3"],"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/soloist-few-shot-task-oriented-dialog-with-a","slug":"soloist-few-shot-task-oriented-dialog-with-a","title":"SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching","date":"2020-05-11","arxiv_id":"2005.05298","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/soloist-few-shot-task-oriented-dialog-with-a#ran","syntology_url":"https://syntology.ai/paper/2005.05298","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.05298"}},"official":null}},{"url":"/paper/towards-efficient-covid-19-ct-annotation-a","slug":"towards-efficient-covid-19-ct-annotation-a","title":"Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation","date":"2020-04-27","arxiv_id":"2004.12537","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":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) · 2 unverified","sample_list":"/paper/towards-efficient-covid-19-ct-annotation-a#ran","syntology_url":"https://syntology.ai/paper/2004.12537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.12537"}},"official":{"repos":["HzFu/COVID19_imaging_AI_paper_list"],"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/defining-benchmarks-for-continual-few-shot","slug":"defining-benchmarks-for-continual-few-shot","title":"Defining Benchmarks for Continual Few-Shot Learning","date":"2020-04-15","arxiv_id":"2004.11967","repositories_listed":2,"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":2,"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/defining-benchmarks-for-continual-few-shot#ran","syntology_url":"https://syntology.ai/paper/2004.11967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.11967"}},"official":{"repos":["AntreasAntoniou/FewShotContinualLearning"],"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":["named_in_paper","official"]}}},{"url":"/paper/unsupervised-few-shot-learning-via","slug":"unsupervised-few-shot-learning-via","title":"Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation","date":"2020-04-13","arxiv_id":"2004.05805","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-few-shot-learning-via#ran","syntology_url":"https://syntology.ai/paper/2004.05805","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.05805"}},"official":{"repos":["WonderSeven/ULDA"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}}],"record_sha256":"5fe10cc155b60190ce884ed98cc64d4788c5b2c55fdc1dc07e90e0b70ca82ed9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}