{"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/transfer-learning/papers/3","list_of":"/task/transfer-learning","task":"Transfer 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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":104,"rows_per_page":100,"rows":[201,300],"of":10307,"counts":{"archive_papers_tagged":10307,"with_a_code_link":3502,"where_syntology_ran_a_sample":692,"not_listed_spam_title":0,"listed":10307,"listed_where_code_ran":692,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":563,"every_run_a_failure_of_syntologys_instrument":129,"listed_with_a_run_with_no_instrument_failure":563,"listed_every_run_a_failure_of_syntologys_instrument":129,"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/transfer-learning","prev":"/task/transfer-learning/papers/2","next":"/task/transfer-learning/papers/4","papers":[{"url":"/paper/large-scale-simple-question-answering-with","slug":"large-scale-simple-question-answering-with","title":"Large-scale Simple Question Answering with Memory Networks","date":"2015-06-05","arxiv_id":"1506.02075","repositories_listed":3,"syntology":null},{"url":"/paper/the-arcade-learning-environment-an-evaluation","slug":"the-arcade-learning-environment-an-evaluation","title":"The Arcade Learning Environment: An Evaluation Platform for General Agents","date":"2012-07-19","arxiv_id":"1207.4708","repositories_listed":3,"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/the-arcade-learning-environment-an-evaluation#ran","syntology_url":"https://syntology.ai/paper/1207.4708","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1207.4708"}},"official":null}},{"url":"/paper/residual-feature-integration-is-sufficient-to","slug":"residual-feature-integration-is-sufficient-to","title":"Residual Feature Integration is Sufficient to Prevent Negative Transfer","date":"2025-05-17","arxiv_id":"2505.11771","repositories_listed":2,"syntology":null},{"url":"/paper/structural-alignment-in-link-prediction","slug":"structural-alignment-in-link-prediction","title":"Structural Alignment in Link Prediction","date":"2025-05-08","arxiv_id":"2505.04939","repositories_listed":2,"syntology":null},{"url":"/paper/molecular-driven-foundation-model-for","slug":"molecular-driven-foundation-model-for","title":"Molecular-driven Foundation Model for Oncologic Pathology","date":"2025-01-28","arxiv_id":"2501.16652","repositories_listed":2,"syntology":null},{"url":"/paper/fta-ftl-a-fine-tuned-aggregation-federated","slug":"fta-ftl-a-fine-tuned-aggregation-federated","title":"FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image Classification","date":"2025-01-06","arxiv_id":"2501.03349","repositories_listed":2,"syntology":null},{"url":"/paper/sample-correlation-for-fingerprinting-deep","slug":"sample-correlation-for-fingerprinting-deep","title":"Sample Correlation for Fingerprinting Deep Face Recognition","date":"2024-12-30","arxiv_id":"2412.20768","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-models-for-colloidal","slug":"deep-learning-models-for-colloidal","title":"Deep Learning Models for Colloidal Nanocrystal Synthesis","date":"2024-12-14","arxiv_id":"2412.10838","repositories_listed":2,"syntology":null},{"url":"/paper/pos-tagging-to-highlight-the-skeletal","slug":"pos-tagging-to-highlight-the-skeletal","title":"POS-tagging to highlight the skeletal structure of sentences","date":"2024-11-21","arxiv_id":"2411.14393","repositories_listed":2,"syntology":null},{"url":"/paper/llm-neo-parameter-efficient-knowledge","slug":"llm-neo-parameter-efficient-knowledge","title":"LLM-Neo: Parameter Efficient Knowledge Distillation for Large Language Models","date":"2024-11-11","arxiv_id":"2411.06839","repositories_listed":2,"syntology":{"n":12,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/llm-neo-parameter-efficient-knowledge#ran","syntology_url":"https://syntology.ai/paper/2411.06839","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.06839"}},"official":null}},{"url":"/paper/lessons-learned-from-a-unifying-empirical","slug":"lessons-learned-from-a-unifying-empirical","title":"Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition","date":"2024-09-24","arxiv_id":"2409.16434","repositories_listed":2,"syntology":null},{"url":"/paper/fafesort-a-fast-and-few-shot-end-to-end","slug":"fafesort-a-fast-and-few-shot-end-to-end","title":"E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processing","date":"2024-09-19","arxiv_id":"2409.13067","repositories_listed":2,"syntology":null},{"url":"/paper/all-in-one-foundational-models-learning","slug":"all-in-one-foundational-models-learning","title":"All-in-one foundational models learning across quantum chemical levels","date":"2024-09-18","arxiv_id":"2409.12015","repositories_listed":2,"syntology":null},{"url":"/paper/adaptive-meta-domain-transfer-learning-amdtl-1","slug":"adaptive-meta-domain-transfer-learning-amdtl-1","title":"Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AI","date":"2024-09-10","arxiv_id":"2409.06800","repositories_listed":2,"syntology":null},{"url":"/paper/efficient-training-of-large-vision-models-via","slug":"efficient-training-of-large-vision-models-via","title":"Efficient Training of Large Vision Models via Advanced Automated Progressive Learning","date":"2024-09-06","arxiv_id":"2410.00350","repositories_listed":2,"syntology":null},{"url":"/paper/comparative-analysis-of-transfer-learning","slug":"comparative-analysis-of-transfer-learning","title":"Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study","date":"2024-08-29","arxiv_id":"2408.16859","repositories_listed":2,"syntology":null},{"url":"/paper/github-is-an-effective-platform-for","slug":"github-is-an-effective-platform-for","title":"GitHub is an effective platform for collaborative and reproducible laboratory research","date":"2024-08-18","arxiv_id":"2408.09344","repositories_listed":2,"syntology":null},{"url":"/paper/adarank-disagreement-based-module-rank","slug":"adarank-disagreement-based-module-rank","title":"AdaRank: Disagreement Based Module Rank Prediction for Low-rank Adaptation","date":"2024-08-16","arxiv_id":"2408.09015","repositories_listed":2,"syntology":null},{"url":"/paper/ecg-fm-an-open-electrocardiogram-foundation","slug":"ecg-fm-an-open-electrocardiogram-foundation","title":"ECG-FM: An Open Electrocardiogram Foundation Model","date":"2024-08-09","arxiv_id":"2408.05178","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"5 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ecg-fm-an-open-electrocardiogram-foundation#ran","syntology_url":"https://syntology.ai/paper/2408.05178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.05178"}},"official":{"repos":["bowang-lab/ecg-fm","jwoo5/fairseq-signals"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/llava-onevision-easy-visual-task-transfer","slug":"llava-onevision-easy-visual-task-transfer","title":"LLaVA-OneVision: Easy Visual Task Transfer","date":"2024-08-06","arxiv_id":"2408.03326","repositories_listed":2,"syntology":null},{"url":"/paper/abdomenatlas-a-large-scale-detailed-annotated","slug":"abdomenatlas-a-large-scale-detailed-annotated","title":"AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking","date":"2024-07-23","arxiv_id":"2407.16697","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/abdomenatlas-a-large-scale-detailed-annotated#ran","syntology_url":"https://syntology.ai/paper/2407.16697","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.16697"}},"official":{"repos":["mrgiovanni/abdomenatlas","mrgiovanni/suprem"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/soft-language-prompts-for-language-transfer","slug":"soft-language-prompts-for-language-transfer","title":"Soft Language Prompts for Language Transfer","date":"2024-07-02","arxiv_id":"2407.02317","repositories_listed":2,"syntology":null},{"url":"/paper/routellm-learning-to-route-llms-with","slug":"routellm-learning-to-route-llms-with","title":"RouteLLM: Learning to Route LLMs with Preference Data","date":"2024-06-26","arxiv_id":"2406.18665","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/routellm-learning-to-route-llms-with#ran","syntology_url":"https://syntology.ai/paper/2406.18665","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.18665"}},"official":{"repos":["lm-sys/routellm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bioscan-5m-a-multimodal-dataset-for-insect","slug":"bioscan-5m-a-multimodal-dataset-for-insect","title":"BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity","date":"2024-06-18","arxiv_id":"2406.12723","repositories_listed":2,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/bioscan-5m-a-multimodal-dataset-for-insect#ran","syntology_url":"https://syntology.ai/paper/2406.12723","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12723"}},"official":{"repos":["bioscan-ml/dataset"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["named_in_paper","official"]}}},{"url":"/paper/large-scale-transfer-learning-for-tabular","slug":"large-scale-transfer-learning-for-tabular","title":"Large Scale Transfer Learning for Tabular Data via Language Modeling","date":"2024-06-17","arxiv_id":"2406.12031","repositories_listed":2,"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/large-scale-transfer-learning-for-tabular#ran","syntology_url":"https://syntology.ai/paper/2406.12031","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12031"}},"official":{"repos":["mlfoundations/rtfm","mlfoundations/tabliblib"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/self-distillation-prototypes-network-learning","slug":"self-distillation-prototypes-network-learning","title":"Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision","date":"2024-06-17","arxiv_id":"2406.11169","repositories_listed":2,"syntology":null},{"url":"/paper/industrial-language-image-dataset-ilid","slug":"industrial-language-image-dataset-ilid","title":"Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial Settings","date":"2024-06-14","arxiv_id":"2406.09637","repositories_listed":2,"syntology":null},{"url":"/paper/unibridge-a-unified-approach-to-cross-lingual-1","slug":"unibridge-a-unified-approach-to-cross-lingual-1","title":"UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages","date":"2024-06-14","arxiv_id":"2406.09717","repositories_listed":2,"syntology":null},{"url":"/paper/m2d-clap-masked-modeling-duo-meets-clap-for","slug":"m2d-clap-masked-modeling-duo-meets-clap-for","title":"M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation","date":"2024-06-04","arxiv_id":"2406.02032","repositories_listed":2,"syntology":null},{"url":"/paper/towards-a-generalist-and-blind-rgb-x-tracker","slug":"towards-a-generalist-and-blind-rgb-x-tracker","title":"XTrack: Multimodal Training Boosts RGB-X Video Object Trackers","date":"2024-05-28","arxiv_id":"2405.17773","repositories_listed":2,"syntology":null},{"url":"/paper/expanding-the-horizon-enabling-hybrid-quantum","slug":"expanding-the-horizon-enabling-hybrid-quantum","title":"Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification","date":"2024-04-30","arxiv_id":"2405.00156","repositories_listed":2,"syntology":null},{"url":"/paper/exploring-pre-trained-general-purpose-audio","slug":"exploring-pre-trained-general-purpose-audio","title":"Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection","date":"2024-04-26","arxiv_id":"2404.17107","repositories_listed":2,"syntology":null},{"url":"/paper/unified-unsupervised-salient-object-detection","slug":"unified-unsupervised-salient-object-detection","title":"Unified Unsupervised Salient Object Detection via Knowledge Transfer","date":"2024-04-23","arxiv_id":"2404.14759","repositories_listed":2,"syntology":{"n":19,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":7,"n_honours":1,"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; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/unified-unsupervised-salient-object-detection#ran","syntology_url":"https://syntology.ai/paper/2404.14759","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14759"}},"official":{"repos":["I2-Multimedia-Lab/A2S-v3"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/lighter-better-faster-multi-source-domain","slug":"lighter-better-faster-multi-source-domain","title":"Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport","date":"2024-04-16","arxiv_id":"2404.10261","repositories_listed":2,"syntology":null},{"url":"/paper/pinnacle-pinn-adaptive-collocation-and","slug":"pinnacle-pinn-adaptive-collocation-and","title":"PINNACLE: PINN Adaptive ColLocation and Experimental points selection","date":"2024-04-11","arxiv_id":"2404.07662","repositories_listed":2,"syntology":{"n":8,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"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) · 7 unverified","sample_list":"/paper/pinnacle-pinn-adaptive-collocation-and#ran","syntology_url":"https://syntology.ai/paper/2404.07662","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07662"}},"official":{"repos":["apivich-h/pinnacle"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/spectral-convolutional-transformer","slug":"spectral-convolutional-transformer","title":"Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis","date":"2024-03-26","arxiv_id":"2403.18063","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/spectral-convolutional-transformer#ran","syntology_url":"https://syntology.ai/paper/2403.18063","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18063"}},"official":{"repos":["badripatro/heracles","badripatro/sct"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/removing-undesirable-concepts-in-text-to","slug":"removing-undesirable-concepts-in-text-to","title":"Removing Undesirable Concepts in Text-to-Image Diffusion Models with Learnable Prompts","date":"2024-03-18","arxiv_id":"2403.12326","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/removing-undesirable-concepts-in-text-to#ran","syntology_url":"https://syntology.ai/paper/2403.12326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12326"}},"official":null}},{"url":"/paper/featup-a-model-agnostic-framework-for","slug":"featup-a-model-agnostic-framework-for","title":"FeatUp: A Model-Agnostic Framework for Features at Any Resolution","date":"2024-03-15","arxiv_id":"2403.10516","repositories_listed":2,"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":0,"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/featup-a-model-agnostic-framework-for#ran","syntology_url":"https://syntology.ai/paper/2403.10516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10516"}},"official":{"repos":["mhamilton723/FeatUp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/fastsam3d-an-efficient-segment-anything-model","slug":"fastsam3d-an-efficient-segment-anything-model","title":"FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images","date":"2024-03-14","arxiv_id":"2403.09827","repositories_listed":2,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":7,"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) · 7 unverified","sample_list":"/paper/fastsam3d-an-efficient-segment-anything-model#ran","syntology_url":"https://syntology.ai/paper/2403.09827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09827"}},"official":{"repos":["arcadelab/fastsam3d"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/can-llms-tuning-methods-work-in-medical","slug":"can-llms-tuning-methods-work-in-medical","title":"Can LLMs' Tuning Methods Work in Medical Multimodal Domain?","date":"2024-03-11","arxiv_id":"2403.06407","repositories_listed":2,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":7,"phrase":"6 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/can-llms-tuning-methods-work-in-medical#ran","syntology_url":"https://syntology.ai/paper/2403.06407","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06407"}},"official":{"repos":["timmy-chan/mile"],"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":["listed","official"]}}},{"url":"/paper/lead-learning-decomposition-for-source-free","slug":"lead-learning-decomposition-for-source-free","title":"LEAD: Learning Decomposition for Source-free Universal Domain Adaptation","date":"2024-03-06","arxiv_id":"2403.03421","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":3,"n_instrument":6,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":9,"phrase":"9 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; 6 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/lead-learning-decomposition-for-source-free#ran","syntology_url":"https://syntology.ai/paper/2403.03421","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.03421"}},"official":{"repos":["ispc-lab/lead"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/transfer-learning-bayesian-optimization-to","slug":"transfer-learning-bayesian-optimization-to","title":"Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays","date":"2024-02-27","arxiv_id":"2402.17704","repositories_listed":2,"syntology":null},{"url":"/paper/clap-learning-transferable-binary-code","slug":"clap-learning-transferable-binary-code","title":"CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision","date":"2024-02-26","arxiv_id":"2402.16928","repositories_listed":2,"syntology":null},{"url":"/paper/global-safe-sequential-learning-via-efficient","slug":"global-safe-sequential-learning-via-efficient","title":"Global Safe Sequential Learning via Efficient Knowledge Transfer","date":"2024-02-22","arxiv_id":"2402.14402","repositories_listed":2,"syntology":null},{"url":"/paper/cmaes-a-simple-yet-practical-python-library","slug":"cmaes-a-simple-yet-practical-python-library","title":"cmaes : A Simple yet Practical Python Library for CMA-ES","date":"2024-02-02","arxiv_id":"2402.01373","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":4,"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, 2 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cmaes-a-simple-yet-practical-python-library#ran","syntology_url":"https://syntology.ai/paper/2402.01373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01373"}},"official":{"repos":["CyberAgentAILab/cmaes"],"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":["listed","official"]}}},{"url":"/paper/transfer-learning-based-autotuning-using","slug":"transfer-learning-based-autotuning-using","title":"Transfer-Learning-Based Autotuning Using Gaussian Copula","date":"2024-01-09","arxiv_id":"2401.04669","repositories_listed":2,"syntology":null},{"url":"/paper/ttms-fast-multi-level-tiny-time-mixers-for","slug":"ttms-fast-multi-level-tiny-time-mixers-for","title":"Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series","date":"2024-01-08","arxiv_id":"2401.03955","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ttms-fast-multi-level-tiny-time-mixers-for#ran","syntology_url":"https://syntology.ai/paper/2401.03955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.03955"}},"official":{"repos":["ibm-granite/granite-tsfm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-class-incremental-learning-with-new","slug":"federated-class-incremental-learning-with-new","title":"Federated Class-Incremental Learning with New-Class Augmented Self-Distillation","date":"2024-01-01","arxiv_id":"2401.00622","repositories_listed":2,"syntology":null},{"url":"/paper/transfer-and-alignment-network-for","slug":"transfer-and-alignment-network-for","title":"Transfer and Alignment Network for Generalized Category Discovery","date":"2023-12-27","arxiv_id":"2312.16467","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":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/transfer-and-alignment-network-for#ran","syntology_url":"https://syntology.ai/paper/2312.16467","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.16467"}},"official":{"repos":["lackel/tan"],"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/timesurl-self-supervised-contrastive-learning","slug":"timesurl-self-supervised-contrastive-learning","title":"TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning","date":"2023-12-25","arxiv_id":"2312.15709","repositories_listed":2,"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":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/timesurl-self-supervised-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2312.15709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.15709"}},"official":{"repos":["Alrash/TimesURL"],"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/kdas3-knowledge-distillation-via-attention","slug":"kdas3-knowledge-distillation-via-attention","title":"KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation","date":"2023-12-13","arxiv_id":"2312.08555","repositories_listed":2,"syntology":null},{"url":"/paper/diff-op3d-bridging-2d-diffusion-for-open-pose","slug":"diff-op3d-bridging-2d-diffusion-for-open-pose","title":"Open-Pose 3D Zero-Shot Learning: Benchmark and Challenges","date":"2023-12-12","arxiv_id":"2312.07039","repositories_listed":2,"syntology":null},{"url":"/paper/facial-beauty-analysis-using-distribution","slug":"facial-beauty-analysis-using-distribution","title":"Facial Beauty Analysis Using Distribution Prediction and CNN Ensembles","date":"2023-12-10","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/pose-guidance-by-supervision-a-framework-for","slug":"pose-guidance-by-supervision-a-framework-for","title":"PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification","date":"2023-12-09","arxiv_id":"2312.05634","repositories_listed":2,"syntology":null},{"url":"/paper/a-scalable-and-generalizable-pathloss-map","slug":"a-scalable-and-generalizable-pathloss-map","title":"A Scalable and Generalizable Pathloss Map Prediction","date":"2023-12-06","arxiv_id":"2312.03950","repositories_listed":2,"syntology":null},{"url":"/paper/side4video-spatial-temporal-side-network-for","slug":"side4video-spatial-temporal-side-network-for","title":"Side4Video: Spatial-Temporal Side Network for Memory-Efficient Image-to-Video Transfer Learning","date":"2023-11-27","arxiv_id":"2311.15769","repositories_listed":2,"syntology":null},{"url":"/paper/eliminating-domain-bias-for-federated","slug":"eliminating-domain-bias-for-federated","title":"Eliminating Domain Bias for Federated Learning in Representation Space","date":"2023-11-25","arxiv_id":"2311.14975","repositories_listed":2,"syntology":{"n":16,"n_ran":12,"n_constructed":2,"n_ran_checked":7,"n_instrument":5,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"12 ran (of which 2 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/eliminating-domain-bias-for-federated#ran","syntology_url":"https://syntology.ai/paper/2311.14975","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14975"}},"official":{"repos":["tsingz0/dbe"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":1,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/lorashear-efficient-large-language-model","slug":"lorashear-efficient-large-language-model","title":"LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery","date":"2023-10-24","arxiv_id":"2310.18356","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/lorashear-efficient-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2310.18356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18356"}},"official":null}},{"url":"/paper/causal-similarity-based-hierarchical-bayesian","slug":"causal-similarity-based-hierarchical-bayesian","title":"Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal Mechanisms","date":"2023-10-19","arxiv_id":"2310.12595","repositories_listed":2,"syntology":null},{"url":"/paper/self-supervised-set-representation-learning","slug":"self-supervised-set-representation-learning","title":"Self-Supervised Dataset Distillation for Transfer Learning","date":"2023-10-10","arxiv_id":"2310.06511","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-set-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2310.06511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.06511"}},"official":{"repos":["db-lee/selfsup_dd"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-simple-and-robust-framework-for-cross","slug":"a-simple-and-robust-framework-for-cross","title":"A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers","date":"2023-10-09","arxiv_id":"2310.05572","repositories_listed":2,"syntology":null},{"url":"/paper/a-survey-of-incremental-transfer-learning","slug":"a-survey-of-incremental-transfer-learning","title":"A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration","date":"2023-09-29","arxiv_id":"2309.17192","repositories_listed":2,"syntology":null},{"url":"/paper/amplifying-pathological-detection-in-eeg","slug":"amplifying-pathological-detection-in-eeg","title":"Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning","date":"2023-09-19","arxiv_id":"2309.10910","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/amplifying-pathological-detection-in-eeg#ran","syntology_url":"https://syntology.ai/paper/2309.10910","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.10910"}},"official":null}},{"url":"/paper/sct-a-simple-baseline-for-parameter-efficient","slug":"sct-a-simple-baseline-for-parameter-efficient","title":"SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels","date":"2023-09-15","arxiv_id":"2309.08513","repositories_listed":2,"syntology":null},{"url":"/paper/ninerec-a-benchmark-dataset-suite-for","slug":"ninerec-a-benchmark-dataset-suite-for","title":"NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation","date":"2023-09-14","arxiv_id":"2309.07705","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ninerec-a-benchmark-dataset-suite-for#ran","syntology_url":"https://syntology.ai/paper/2309.07705","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.07705"}},"official":{"repos":["westlake-repl/ninerec","anonymous-ninerec/ninerec"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dept-decomposed-prompt-tuning-for-parameter","slug":"dept-decomposed-prompt-tuning-for-parameter","title":"DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning","date":"2023-09-11","arxiv_id":"2309.05173","repositories_listed":2,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"8 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dept-decomposed-prompt-tuning-for-parameter#ran","syntology_url":"https://syntology.ai/paper/2309.05173","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05173"}},"official":{"repos":["zhengxiangshi/dept"],"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/reprogramming-under-constraints-revisiting","slug":"reprogramming-under-constraints-revisiting","title":"Uncovering the Hidden Cost of Model Compression","date":"2023-08-29","arxiv_id":"2308.14969","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/reprogramming-under-constraints-revisiting#ran","syntology_url":"https://syntology.ai/paper/2308.14969","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14969"}},"official":{"repos":["landskape-ai/reprogram_lt"],"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/ceimven-an-approach-of-cutting-edge","slug":"ceimven-an-approach-of-cutting-edge","title":"CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and Classification from Ultrasound Images","date":"2023-08-25","arxiv_id":"2308.13356","repositories_listed":2,"syntology":null},{"url":"/paper/expel-llm-agents-are-experiential-learners","slug":"expel-llm-agents-are-experiential-learners","title":"ExpeL: LLM Agents Are Experiential Learners","date":"2023-08-20","arxiv_id":"2308.10144","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/expel-llm-agents-are-experiential-learners#ran","syntology_url":"https://syntology.ai/paper/2308.10144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.10144"}},"official":{"repos":["LeapLabTHU/ExpeL"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/stem-unleashing-the-power-of-embeddings-for","slug":"stem-unleashing-the-power-of-embeddings-for","title":"STEM: Unleashing the Power of Embeddings for Multi-task Recommendation","date":"2023-08-16","arxiv_id":"2308.13537","repositories_listed":2,"syntology":null},{"url":"/paper/surface-masked-autoencoder-self-supervision","slug":"surface-masked-autoencoder-self-supervision","title":"Spatio-Temporal Encoding of Brain Dynamics with Surface Masked Autoencoders","date":"2023-08-10","arxiv_id":"2308.05474","repositories_listed":2,"syntology":null},{"url":"/paper/ensemble-distillation-network-learning-robust","slug":"ensemble-distillation-network-learning-robust","title":"Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision","date":"2023-08-05","arxiv_id":"2308.02774","repositories_listed":2,"syntology":null},{"url":"/paper/lp-musiccaps-llm-based-pseudo-music","slug":"lp-musiccaps-llm-based-pseudo-music","title":"LP-MusicCaps: LLM-Based Pseudo Music Captioning","date":"2023-07-31","arxiv_id":"2307.16372","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/lp-musiccaps-llm-based-pseudo-music#ran","syntology_url":"https://syntology.ai/paper/2307.16372","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.16372"}},"official":{"repos":["seungheondoh/lp-music-caps"],"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":["listed","official"]}}},{"url":"/paper/revisiting-invariances-and-introducing-priors","slug":"revisiting-invariances-and-introducing-priors","title":"Revisiting invariances and introducing priors in Gromov-Wasserstein distances","date":"2023-07-19","arxiv_id":"2307.10093","repositories_listed":2,"syntology":null},{"url":"/paper/class-relation-knowledge-distillation-for","slug":"class-relation-knowledge-distillation-for","title":"Class-relation Knowledge Distillation for Novel Class Discovery","date":"2023-07-18","arxiv_id":"2307.09158","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/class-relation-knowledge-distillation-for#ran","syntology_url":"https://syntology.ai/paper/2307.09158","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09158"}},"official":{"repos":["kleinzcy/cr-kd-ncd"],"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/feature-embeddings-from-large-scale-acoustic","slug":"feature-embeddings-from-large-scale-acoustic","title":"Global birdsong embeddings enable superior transfer learning for bioacoustic classification","date":"2023-07-12","arxiv_id":"2307.06292","repositories_listed":2,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/feature-embeddings-from-large-scale-acoustic#ran","syntology_url":"https://syntology.ai/paper/2307.06292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.06292"}},"official":{"repos":["google-research/chirp","google-research/perch"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/mdvit-multi-domain-vision-transformer-for","slug":"mdvit-multi-domain-vision-transformer-for","title":"MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets","date":"2023-07-05","arxiv_id":"2307.02100","repositories_listed":2,"syntology":null},{"url":"/paper/sampling-weights-of-deep-neural-networks-1","slug":"sampling-weights-of-deep-neural-networks-1","title":"Sampling weights of deep neural networks","date":"2023-06-29","arxiv_id":"2306.16830","repositories_listed":2,"syntology":null},{"url":"/paper/minigrid-miniworld-modular-customizable-1","slug":"minigrid-miniworld-modular-customizable-1","title":"Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks","date":"2023-06-24","arxiv_id":"2306.13831","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/minigrid-miniworld-modular-customizable-1#ran","syntology_url":"https://syntology.ai/paper/2306.13831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.13831"}},"official":{"repos":["farama-foundation/minigrid","farama-foundation/miniworld"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/segment-any-point-cloud-sequences-by","slug":"segment-any-point-cloud-sequences-by","title":"Segment Any Point Cloud Sequences by Distilling Vision Foundation Models","date":"2023-06-15","arxiv_id":"2306.09347","repositories_listed":2,"syntology":{"n":24,"n_ran":18,"n_constructed":3,"n_ran_checked":17,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":6,"phrase":"18 ran (of which 3 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/segment-any-point-cloud-sequences-by#ran","syntology_url":"https://syntology.ai/paper/2306.09347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.09347"}},"official":{"repos":["youquanl/segment-any-point-cloud","xiaoaoran/SynLiDAR"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/libero-benchmarking-knowledge-transfer-for","slug":"libero-benchmarking-knowledge-transfer-for","title":"LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning","date":"2023-06-05","arxiv_id":"2306.03310","repositories_listed":2,"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":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/libero-benchmarking-knowledge-transfer-for#ran","syntology_url":"https://syntology.ai/paper/2306.03310","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03310"}},"official":null}},{"url":"/paper/training-like-a-medical-resident-universal","slug":"training-like-a-medical-resident-universal","title":"Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation","date":"2023-06-04","arxiv_id":"2306.02416","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"9 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/training-like-a-medical-resident-universal#ran","syntology_url":"https://syntology.ai/paper/2306.02416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02416"}},"official":{"repos":["yhygao/universal-medical-image-segmentation"],"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/a-transfer-learning-and-explainable-solution","slug":"a-transfer-learning-and-explainable-solution","title":"A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images","date":"2023-05-29","arxiv_id":"2305.18489","repositories_listed":2,"syntology":null},{"url":"/paper/bert4cmr-cross-market-recommendation-with","slug":"bert4cmr-cross-market-recommendation-with","title":"Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from Transformer","date":"2023-05-24","arxiv_id":"2305.15145","repositories_listed":2,"syntology":null},{"url":"/paper/creator-disentangling-abstract-and-concrete","slug":"creator-disentangling-abstract-and-concrete","title":"CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models","date":"2023-05-23","arxiv_id":"2305.14318","repositories_listed":2,"syntology":null},{"url":"/paper/crosslingual-transfer-learning-for-low","slug":"crosslingual-transfer-learning-for-low","title":"Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs","date":"2023-05-22","arxiv_id":"2305.12818","repositories_listed":2,"syntology":null},{"url":"/paper/tune-mode-convbn-blocks-for-efficient","slug":"tune-mode-convbn-blocks-for-efficient","title":"Efficient ConvBN Blocks for Transfer Learning and Beyond","date":"2023-05-19","arxiv_id":"2305.11624","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tune-mode-convbn-blocks-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2305.11624","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11624"}},"official":{"repos":["apple/ml-tune-mode-convbn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/real-time-flying-object-detection-with-yolov8","slug":"real-time-flying-object-detection-with-yolov8","title":"Real-Time Flying Object Detection with YOLOv8","date":"2023-05-17","arxiv_id":"2305.09972","repositories_listed":2,"syntology":null},{"url":"/paper/spider-gan-leveraging-friendly-neighbors-to","slug":"spider-gan-leveraging-friendly-neighbors-to","title":"Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training","date":"2023-05-12","arxiv_id":"2305.07613","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 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/spider-gan-leveraging-friendly-neighbors-to#ran","syntology_url":"https://syntology.ai/paper/2305.07613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.07613"}},"official":{"repos":["darthsid95/clean-sid","darthsid95/spiderstylegan"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/shotgun-crystal-structure-prediction-using","slug":"shotgun-crystal-structure-prediction-using","title":"Shotgun crystal structure prediction using machine-learned formation energies","date":"2023-05-03","arxiv_id":"2305.02158","repositories_listed":2,"syntology":null},{"url":"/paper/real-time-safety-assessment-of-dynamic","slug":"real-time-safety-assessment-of-dynamic","title":"Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques","date":"2023-04-25","arxiv_id":"2304.12583","repositories_listed":2,"syntology":null},{"url":"/paper/greekbart-the-first-pretrained-greek-sequence","slug":"greekbart-the-first-pretrained-greek-sequence","title":"GreekBART: The First Pretrained Greek Sequence-to-Sequence Model","date":"2023-04-03","arxiv_id":"2304.00869","repositories_listed":2,"syntology":null},{"url":"/paper/polytuplet-loss-a-reverse-approach-to","slug":"polytuplet-loss-a-reverse-approach-to","title":"Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet Loss","date":"2023-04-03","arxiv_id":"2304.01046","repositories_listed":2,"syntology":null},{"url":"/paper/quantifying-the-impact-of-data","slug":"quantifying-the-impact-of-data","title":"Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models","date":"2023-03-28","arxiv_id":"2304.06033","repositories_listed":2,"syntology":null},{"url":"/paper/visual-representation-learning-from-unlabeled","slug":"visual-representation-learning-from-unlabeled","title":"ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders","date":"2023-03-21","arxiv_id":"2303.12001","repositories_listed":2,"syntology":null},{"url":"/paper/trainable-projected-gradient-method-for","slug":"trainable-projected-gradient-method-for","title":"Trainable Projected Gradient Method for Robust Fine-tuning","date":"2023-03-19","arxiv_id":"2303.10720","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/trainable-projected-gradient-method-for#ran","syntology_url":"https://syntology.ai/paper/2303.10720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.10720"}},"official":{"repos":["potatotian/tpgm"],"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/marginalia-and-machine-learning-handwritten","slug":"marginalia-and-machine-learning-handwritten","title":"Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognition","date":"2023-03-10","arxiv_id":"2303.05929","repositories_listed":2,"syntology":null},{"url":"/paper/scaling-up-3d-kernels-with-bayesian-frequency","slug":"scaling-up-3d-kernels-with-bayesian-frequency","title":"Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation","date":"2023-03-10","arxiv_id":"2303.05785","repositories_listed":2,"syntology":null},{"url":"/paper/mixspeech-cross-modality-self-learning-with","slug":"mixspeech-cross-modality-self-learning-with","title":"MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition","date":"2023-03-09","arxiv_id":"2303.05309","repositories_listed":2,"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":3,"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/mixspeech-cross-modality-self-learning-with#ran","syntology_url":"https://syntology.ai/paper/2303.05309","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.05309"}},"official":{"repos":["exgc/avmust-ted"],"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/pay-less-but-get-more-a-dual-attention-based","slug":"pay-less-but-get-more-a-dual-attention-based","title":"Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots","date":"2023-03-02","arxiv_id":"2303.00986","repositories_listed":2,"syntology":null}],"record_sha256":"182941118b72e05c9b3080e4b53804b69f3f6fb5467dcf168431fa440565d930","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}