{"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/attribute/papers/2","list_of":"/task/attribute","task":"Attribute","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":2,"pages_in_order":54,"rows_per_page":100,"rows":[101,200],"of":5387,"counts":{"archive_papers_tagged":5387,"with_a_code_link":1923,"where_syntology_ran_a_sample":475,"not_listed_spam_title":0,"listed":5387,"listed_where_code_ran":475,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":387,"every_run_a_failure_of_syntologys_instrument":88,"listed_with_a_run_with_no_instrument_failure":387,"listed_every_run_a_failure_of_syntologys_instrument":88,"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/attribute","prev":"/task/attribute","next":"/task/attribute/papers/3","papers":[{"url":"/paper/transferable-adversarial-face-attack-with","slug":"transferable-adversarial-face-attack-with","title":"Transferable Adversarial Face Attack with Text Controlled Attribute","date":"2024-12-16","arxiv_id":"2412.11735","repositories_listed":2,"syntology":null},{"url":"/paper/laion-sg-an-enhanced-large-scale-dataset-for","slug":"laion-sg-an-enhanced-large-scale-dataset-for","title":"LAION-SG: An Enhanced Large-Scale Dataset for Training Complex Image-Text Models with Structural Annotations","date":"2024-12-11","arxiv_id":"2412.08580","repositories_listed":2,"syntology":{"n":23,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":11,"n_honours":0,"n_violates":2,"n_no_contract":8,"n_pointer_only":13,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 2 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/laion-sg-an-enhanced-large-scale-dataset-for#ran","syntology_url":"https://syntology.ai/paper/2412.08580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.08580"}},"official":{"repos":["mengcye/LAION-SG"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/towards-learning-to-reason-comparing-llms","slug":"towards-learning-to-reason-comparing-llms","title":"Towards Learning to Reason: Comparing LLMs with Neuro-Symbolic on Arithmetic Relations in Abstract Reasoning","date":"2024-12-07","arxiv_id":"2412.05586","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-learning-to-reason-comparing-llms#ran","syntology_url":"https://syntology.ai/paper/2412.05586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.05586"}},"official":{"repos":["ibm/raven-large-language-models"],"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/itercomp-iterative-composition-aware-feedback","slug":"itercomp-iterative-composition-aware-feedback","title":"IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation","date":"2024-10-09","arxiv_id":"2410.07171","repositories_listed":2,"syntology":{"n":18,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":7,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/itercomp-iterative-composition-aware-feedback#ran","syntology_url":"https://syntology.ai/paper/2410.07171","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.07171"}},"official":{"repos":["yangling0818/itercomp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/large-language-model-empowered-embedding","slug":"large-language-model-empowered-embedding","title":"LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation","date":"2024-09-30","arxiv_id":"2409.19925","repositories_listed":2,"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/large-language-model-empowered-embedding#ran","syntology_url":"https://syntology.ai/paper/2409.19925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19925"}},"official":{"repos":["applied-machine-learning-lab/llmemb","liuqidong07/LLMEmb"],"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/comicap-a-vlms-pipeline-for-dense-captioning","slug":"comicap-a-vlms-pipeline-for-dense-captioning","title":"ComiCap: A VLMs pipeline for dense captioning of Comic Panels","date":"2024-09-24","arxiv_id":"2409.16159","repositories_listed":2,"syntology":null},{"url":"/paper/pedestrian-attribute-recognition-a-new","slug":"pedestrian-attribute-recognition-a-new","title":"Pedestrian Attribute Recognition: A New Benchmark Dataset and A Large Language Model Augmented Framework","date":"2024-08-19","arxiv_id":"2408.09720","repositories_listed":2,"syntology":null},{"url":"/paper/evolver-chain-of-evolution-prompting-to-boost","slug":"evolver-chain-of-evolution-prompting-to-boost","title":"Evolver: Chain-of-Evolution Prompting to Boost Large Multimodal Models for Hateful Meme Detection","date":"2024-07-30","arxiv_id":"2407.21004","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/evolver-chain-of-evolution-prompting-to-boost#ran","syntology_url":"https://syntology.ai/paper/2407.21004","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.21004"}},"official":{"repos":["infaaa/evolver"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-synthetic-dataset-for-personal-attribute","slug":"a-synthetic-dataset-for-personal-attribute","title":"A Synthetic Dataset for Personal Attribute Inference","date":"2024-06-11","arxiv_id":"2406.07217","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/a-synthetic-dataset-for-personal-attribute#ran","syntology_url":"https://syntology.ai/paper/2406.07217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.07217"}},"official":{"repos":["eth-sri/synthpai"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/mvgamba-unify-3d-content-generation-as-state","slug":"mvgamba-unify-3d-content-generation-as-state","title":"MVGamba: Unify 3D Content Generation as State Space Sequence Modeling","date":"2024-06-10","arxiv_id":"2406.06367","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mvgamba-unify-3d-content-generation-as-state#ran","syntology_url":"https://syntology.ai/paper/2406.06367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.06367"}},"official":{"repos":["skyworkai/mvgamba"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/sparsedrive-end-to-end-autonomous-driving-via","slug":"sparsedrive-end-to-end-autonomous-driving-via","title":"SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation","date":"2024-05-30","arxiv_id":"2405.19620","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sparsedrive-end-to-end-autonomous-driving-via#ran","syntology_url":"https://syntology.ai/paper/2405.19620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.19620"}},"official":{"repos":["swc-17/sparsedrive"],"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/raccoon-remove-add-and-change-video-content","slug":"raccoon-remove-add-and-change-video-content","title":"RACCooN: A Versatile Instructional Video Editing Framework with Auto-Generated Narratives","date":"2024-05-28","arxiv_id":"2405.18406","repositories_listed":2,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/raccoon-remove-add-and-change-video-content#ran","syntology_url":"https://syntology.ai/paper/2405.18406","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.18406"}},"official":{"repos":["jaehong31/raccoon"],"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":["listed","official"]}}},{"url":"/paper/optimal-group-fair-classifiers-from-linear","slug":"optimal-group-fair-classifiers-from-linear","title":"A Unified Post-Processing Framework for Group Fairness in Classification","date":"2024-05-07","arxiv_id":"2405.04025","repositories_listed":2,"syntology":null},{"url":"/paper/the-curse-of-diversity-in-ensemble-based","slug":"the-curse-of-diversity-in-ensemble-based","title":"The Curse of Diversity in Ensemble-Based Exploration","date":"2024-05-07","arxiv_id":"2405.04342","repositories_listed":2,"syntology":{"n":16,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":13,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/the-curse-of-diversity-in-ensemble-based#ran","syntology_url":"https://syntology.ai/paper/2405.04342","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.04342"}},"official":{"repos":["zhixuan-lin/ensemble-rl-continuous","zhixuan-lin/ensemble-rl-discrete"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":13,"ran_from_kinds":["official"]}}},{"url":"/paper/peach-pretrained-embedding-explanation-across","slug":"peach-pretrained-embedding-explanation-across","title":"PEACH: Pretrained-embedding Explanation Across Contextual and Hierarchical Structure","date":"2024-04-21","arxiv_id":"2404.13645","repositories_listed":2,"syntology":null},{"url":"/paper/comat-aligning-text-to-image-diffusion-model","slug":"comat-aligning-text-to-image-diffusion-model","title":"CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching","date":"2024-04-04","arxiv_id":"2404.03653","repositories_listed":2,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":9,"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) · 4 unverified","sample_list":"/paper/comat-aligning-text-to-image-diffusion-model#ran","syntology_url":"https://syntology.ai/paper/2404.03653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03653"}},"official":{"repos":["caraj7/comat"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/llm-attributor-interactive-visual-attribution","slug":"llm-attributor-interactive-visual-attribution","title":"LLM Attributor: Interactive Visual Attribution for LLM Generation","date":"2024-04-01","arxiv_id":"2404.01361","repositories_listed":2,"syntology":null},{"url":"/paper/a-dataset-for-pharmacovigilance-in-german","slug":"a-dataset-for-pharmacovigilance-in-german","title":"A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages","date":"2024-03-27","arxiv_id":"2403.18336","repositories_listed":2,"syntology":null},{"url":"/paper/mitigating-hallucinations-in-large-vision","slug":"mitigating-hallucinations-in-large-vision","title":"Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding","date":"2024-03-27","arxiv_id":"2403.18715","repositories_listed":2,"syntology":{"n":8,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/mitigating-hallucinations-in-large-vision#ran","syntology_url":"https://syntology.ai/paper/2403.18715","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18715"}},"official":{"repos":["p1k0pan/ICD"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/hac-hash-grid-assisted-context-for-3d","slug":"hac-hash-grid-assisted-context-for-3d","title":"HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression","date":"2024-03-21","arxiv_id":"2403.14530","repositories_listed":2,"syntology":null},{"url":"/paper/facexformer-a-unified-transformer-for-facial","slug":"facexformer-a-unified-transformer-for-facial","title":"FaceXFormer: A Unified Transformer for Facial Analysis","date":"2024-03-19","arxiv_id":"2403.12960","repositories_listed":2,"syntology":null},{"url":"/paper/angry-men-sad-women-large-language-models","slug":"angry-men-sad-women-large-language-models","title":"Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution","date":"2024-03-05","arxiv_id":"2403.03121","repositories_listed":2,"syntology":null},{"url":"/paper/learning-group-activity-features-through","slug":"learning-group-activity-features-through","title":"Learning Group Activity Features Through Person Attribute Prediction","date":"2024-03-05","arxiv_id":"2403.02753","repositories_listed":2,"syntology":null},{"url":"/paper/learning-to-generate-instruction-tuning","slug":"learning-to-generate-instruction-tuning","title":"Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation","date":"2024-02-28","arxiv_id":"2402.18334","repositories_listed":2,"syntology":null},{"url":"/paper/multilinear-mixture-of-experts-scalable","slug":"multilinear-mixture-of-experts-scalable","title":"Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization","date":"2024-02-19","arxiv_id":"2402.12550","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":1,"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 1 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/multilinear-mixture-of-experts-scalable#ran","syntology_url":"https://syntology.ai/paper/2402.12550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12550"}},"official":{"repos":["james-oldfield/mmoe","james-oldfield/mumoe"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/attnlrp-attention-aware-layer-wise-relevance","slug":"attnlrp-attention-aware-layer-wise-relevance","title":"AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers","date":"2024-02-08","arxiv_id":"2402.05602","repositories_listed":2,"syntology":null},{"url":"/paper/shield-an-evaluation-benchmark-for-face","slug":"shield-an-evaluation-benchmark-for-face","title":"SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models","date":"2024-02-06","arxiv_id":"2402.04178","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":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) · 0 unverified","sample_list":"/paper/shield-an-evaluation-benchmark-for-face#ran","syntology_url":"https://syntology.ai/paper/2402.04178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.04178"}},"official":{"repos":["laiyingxin2/shield"],"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/forgery-aware-adaptive-transformer-for","slug":"forgery-aware-adaptive-transformer-for","title":"Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection","date":"2023-12-27","arxiv_id":"2312.16649","repositories_listed":2,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"6 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/forgery-aware-adaptive-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2312.16649","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.16649"}},"official":{"repos":["Michel-liu/FatFormer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"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/pedestrian-attribute-recognition-via-clip","slug":"pedestrian-attribute-recognition-via-clip","title":"Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion","date":"2023-12-17","arxiv_id":"2312.10692","repositories_listed":2,"syntology":null},{"url":"/paper/chat-3d-v2-bridging-3d-scene-and-large","slug":"chat-3d-v2-bridging-3d-scene-and-large","title":"Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers","date":"2023-12-13","arxiv_id":"2312.08168","repositories_listed":2,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chat-3d-v2-bridging-3d-scene-and-large#ran","syntology_url":"https://syntology.ai/paper/2312.08168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08168"}},"official":{"repos":["chat-3d/chat-3d-v2"],"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":["listed","official"]}}},{"url":"/paper/open-vocabulary-segmentation-with-semantic","slug":"open-vocabulary-segmentation-with-semantic","title":"Open-Vocabulary Segmentation with Semantic-Assisted Calibration","date":"2023-12-07","arxiv_id":"2312.04089","repositories_listed":2,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/open-vocabulary-segmentation-with-semantic#ran","syntology_url":"https://syntology.ai/paper/2312.04089","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.04089"}},"official":{"repos":["workforai/scan","yongliu20/SCAN"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/gaussian-flow-4d-reconstruction-with-dynamic","slug":"gaussian-flow-4d-reconstruction-with-dynamic","title":"Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle","date":"2023-12-06","arxiv_id":"2312.03431","repositories_listed":2,"syntology":null},{"url":"/paper/sequencepar-understanding-pedestrian","slug":"sequencepar-understanding-pedestrian","title":"SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm","date":"2023-12-04","arxiv_id":"2312.01640","repositories_listed":2,"syntology":null},{"url":"/paper/semqa-semi-extractive-multi-source-question","slug":"semqa-semi-extractive-multi-source-question","title":"SEMQA: Semi-Extractive Multi-Source Question Answering","date":"2023-11-08","arxiv_id":"2311.04886","repositories_listed":2,"syntology":null},{"url":"/paper/salient-object-detection-in-rgb-d-videos","slug":"salient-object-detection-in-rgb-d-videos","title":"Salient Object Detection in RGB-D Videos","date":"2023-10-24","arxiv_id":"2310.15482","repositories_listed":2,"syntology":null},{"url":"/paper/reward-augmented-decoding-efficient","slug":"reward-augmented-decoding-efficient","title":"Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model","date":"2023-10-14","arxiv_id":"2310.09520","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 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) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/reward-augmented-decoding-efficient#ran","syntology_url":"https://syntology.ai/paper/2310.09520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09520"}},"official":{"repos":["haikangdeng/RAD"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/understanding-predicting-and-better-resolving-1","slug":"understanding-predicting-and-better-resolving-1","title":"Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL","date":"2023-10-06","arxiv_id":"2310.04411","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":4,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 4 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/understanding-predicting-and-better-resolving-1#ran","syntology_url":"https://syntology.ai/paper/2310.04411","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.04411"}},"official":{"repos":["yueyang130/seem"],"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/halle-switch-rethinking-and-controlling","slug":"halle-switch-rethinking-and-controlling","title":"HallE-Control: Controlling Object Hallucination in Large Multimodal Models","date":"2023-10-03","arxiv_id":"2310.01779","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/halle-switch-rethinking-and-controlling#ran","syntology_url":"https://syntology.ai/paper/2310.01779","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01779"}},"official":{"repos":["bronyayang/HallE_Switch","bronyayang/halle_control"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/hyperformer-enhancing-entity-and-relation","slug":"hyperformer-enhancing-entity-and-relation","title":"HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion","date":"2023-08-12","arxiv_id":"2308.06512","repositories_listed":2,"syntology":null},{"url":"/paper/zyn-zero-shot-reward-models-with-yes-no","slug":"zyn-zero-shot-reward-models-with-yes-no","title":"ZYN: Zero-Shot Reward Models with Yes-No Questions for RLAIF","date":"2023-08-11","arxiv_id":"2308.06385","repositories_listed":2,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/zyn-zero-shot-reward-models-with-yes-no#ran","syntology_url":"https://syntology.ai/paper/2308.06385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06385"}},"official":{"repos":["anon23423589675234/zero-shot-reward-models","vicgalle/zero-shot-reward-models"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/a-solution-to-co-occurrence-bias-attributes","slug":"a-solution-to-co-occurrence-bias-attributes","title":"A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition","date":"2023-07-28","arxiv_id":"2307.15252","repositories_listed":2,"syntology":null},{"url":"/paper/controllable-generation-of-dialogue-acts-for","slug":"controllable-generation-of-dialogue-acts-for","title":"Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Ranking","date":"2023-07-26","arxiv_id":"2307.14440","repositories_listed":2,"syntology":null},{"url":"/paper/causal-fair-machine-learning-via-rank","slug":"causal-fair-machine-learning-via-rank","title":"Causal Fair Machine Learning via Rank-Preserving Interventional Distributions","date":"2023-07-24","arxiv_id":"2307.12797","repositories_listed":2,"syntology":null},{"url":"/paper/blendface-re-designing-identity-encoders-for","slug":"blendface-re-designing-identity-encoders-for","title":"BlendFace: Re-designing Identity Encoders for Face-Swapping","date":"2023-07-20","arxiv_id":"2307.10854","repositories_listed":2,"syntology":{"n":9,"n_ran":5,"n_constructed":3,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":9,"phrase":"5 ran (of which 3 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) · 4 unverified","sample_list":"/paper/blendface-re-designing-identity-encoders-for#ran","syntology_url":"https://syntology.ai/paper/2307.10854","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.10854"}},"official":{"repos":["mapooon/blendface"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/disco-disentangled-control-for-referring","slug":"disco-disentangled-control-for-referring","title":"DisCo: Disentangled Control for Realistic Human Dance Generation","date":"2023-06-30","arxiv_id":"2307.00040","repositories_listed":2,"syntology":null},{"url":"/paper/restart-sampling-for-improving-generative","slug":"restart-sampling-for-improving-generative","title":"Restart Sampling for Improving Generative Processes","date":"2023-06-26","arxiv_id":"2306.14878","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":1,"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/restart-sampling-for-improving-generative#ran","syntology_url":"https://syntology.ai/paper/2306.14878","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.14878"}},"official":{"repos":["newbeeer/diffusion_restart_sampling"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/preserving-commonsense-knowledge-from-pre","slug":"preserving-commonsense-knowledge-from-pre","title":"Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference","date":"2023-06-19","arxiv_id":"2306.10790","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":2,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/preserving-commonsense-knowledge-from-pre#ran","syntology_url":"https://syntology.ai/paper/2306.10790","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.10790"}},"official":{"repos":["qianlima-lab/cet","zzz47zzz/cet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/linguistic-binding-in-diffusion-models-1","slug":"linguistic-binding-in-diffusion-models-1","title":"Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment","date":"2023-06-15","arxiv_id":"2306.08877","repositories_listed":2,"syntology":null},{"url":"/paper/musecoco-generating-symbolic-music-from-text","slug":"musecoco-generating-symbolic-music-from-text","title":"MuseCoco: Generating Symbolic Music from Text","date":"2023-05-31","arxiv_id":"2306.00110","repositories_listed":2,"syntology":null},{"url":"/paper/styleavatar3d-leveraging-image-text-diffusion","slug":"styleavatar3d-leveraging-image-text-diffusion","title":"StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation","date":"2023-05-30","arxiv_id":"2305.19012","repositories_listed":2,"syntology":null},{"url":"/paper/mix-of-show-decentralized-low-rank-adaptation-1","slug":"mix-of-show-decentralized-low-rank-adaptation-1","title":"Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models","date":"2023-05-29","arxiv_id":"2305.18292","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":4,"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/mix-of-show-decentralized-low-rank-adaptation-1#ran","syntology_url":"https://syntology.ai/paper/2305.18292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18292"}},"official":null}},{"url":"/paper/caila-concept-aware-intra-layer-adapters-for","slug":"caila-concept-aware-intra-layer-adapters-for","title":"CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot Learning","date":"2023-05-26","arxiv_id":"2305.16681","repositories_listed":2,"syntology":null},{"url":"/paper/heterogeneous-value-evaluation-for-large","slug":"heterogeneous-value-evaluation-for-large","title":"Heterogeneous Value Alignment Evaluation for Large Language Models","date":"2023-05-26","arxiv_id":"2305.17147","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":0,"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/heterogeneous-value-evaluation-for-large#ran","syntology_url":"https://syntology.ai/paper/2305.17147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.17147"}},"official":{"repos":["zowiezhang/a2ehv","zowiezhang/hvae"],"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/x-llm-bootstrapping-advanced-large-language","slug":"x-llm-bootstrapping-advanced-large-language","title":"X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages","date":"2023-05-07","arxiv_id":"2305.04160","repositories_listed":2,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"8 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/x-llm-bootstrapping-advanced-large-language#ran","syntology_url":"https://syntology.ai/paper/2305.04160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04160"}},"official":null}},{"url":"/paper/explaining-rl-decisions-with-trajectories","slug":"explaining-rl-decisions-with-trajectories","title":"Explaining RL Decisions with Trajectories","date":"2023-05-06","arxiv_id":"2305.04073","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 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) · 1 unverified","sample_list":"/paper/explaining-rl-decisions-with-trajectories#ran","syntology_url":"https://syntology.ai/paper/2305.04073","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04073"}},"official":{"repos":["shripaddeshmukh/xrl_with_trajectories"],"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/imagenet-e-benchmarking-neural-network","slug":"imagenet-e-benchmarking-neural-network","title":"ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing","date":"2023-03-30","arxiv_id":"2303.17096","repositories_listed":2,"syntology":null},{"url":"/paper/discovering-interpretable-directions-in-the","slug":"discovering-interpretable-directions-in-the","title":"Discovering Interpretable Directions in the Semantic Latent Space of Diffusion Models","date":"2023-03-20","arxiv_id":"2303.11073","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/discovering-interpretable-directions-in-the#ran","syntology_url":"https://syntology.ai/paper/2303.11073","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.11073"}},"official":{"repos":["renhaa/semantic-diffusion"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/editing-implicit-assumptions-in-text-to-image","slug":"editing-implicit-assumptions-in-text-to-image","title":"Editing Implicit Assumptions in Text-to-Image Diffusion Models","date":"2023-03-14","arxiv_id":"2303.08084","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":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/editing-implicit-assumptions-in-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2303.08084","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.08084"}},"official":{"repos":["bahjat-kawar/time-diffusion"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/slca-slow-learner-with-classifier-alignment","slug":"slca-slow-learner-with-classifier-alignment","title":"SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model","date":"2023-03-09","arxiv_id":"2303.05118","repositories_listed":2,"syntology":null},{"url":"/paper/multimodal-recommender-systems-a-survey","slug":"multimodal-recommender-systems-a-survey","title":"Multimodal Recommender Systems: A Survey","date":"2023-02-08","arxiv_id":"2302.03883","repositories_listed":2,"syntology":null},{"url":"/paper/impact-of-the-euro-2020-championship-on-the","slug":"impact-of-the-euro-2020-championship-on-the","title":"Impact of the Euro 2020 championship on the spread of COVID-19","date":"2023-01-18","arxiv_id":"2301.07659","repositories_listed":2,"syntology":null},{"url":"/paper/hard-sample-aware-network-for-contrastive","slug":"hard-sample-aware-network-for-contrastive","title":"Hard Sample Aware Network for Contrastive Deep Graph Clustering","date":"2022-12-16","arxiv_id":"2212.08665","repositories_listed":2,"syntology":null},{"url":"/paper/enhanced-training-of-query-based-object","slug":"enhanced-training-of-query-based-object","title":"Enhanced Training of Query-Based Object Detection via Selective Query Recollection","date":"2022-12-15","arxiv_id":"2212.07593","repositories_listed":2,"syntology":null},{"url":"/paper/practical-and-scalable-simulations-of-non","slug":"practical-and-scalable-simulations-of-non","title":"Practical and scalable simulations of non-Markovian stochastic processes and temporal networks with individual node properties","date":"2022-12-09","arxiv_id":"2212.05059","repositories_listed":2,"syntology":null},{"url":"/paper/contrastive-deep-graph-clustering-with","slug":"contrastive-deep-graph-clustering-with","title":"GraphLearner: Graph Node Clustering with Fully Learnable Augmentation","date":"2022-12-07","arxiv_id":"2212.03559","repositories_listed":2,"syntology":null},{"url":"/paper/a-survey-of-deep-graph-clustering-taxonomy","slug":"a-survey-of-deep-graph-clustering-taxonomy","title":"A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource","date":"2022-11-23","arxiv_id":"2211.12875","repositories_listed":2,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-survey-of-deep-graph-clustering-taxonomy#ran","syntology_url":"https://syntology.ai/paper/2211.12875","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.12875"}},"official":{"repos":["marigoldwu/a-unified-framework-for-deep-attribute-graph-clustering","yueliu1999/awesome-deep-graph-clustering"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/credanno-annotation-exploitation-in-self","slug":"credanno-annotation-exploitation-in-self","title":"cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis","date":"2022-10-28","arxiv_id":"2210.16097","repositories_listed":2,"syntology":null},{"url":"/paper/towards-the-detection-of-diffusion-model","slug":"towards-the-detection-of-diffusion-model","title":"Towards the Detection of Diffusion Model Deepfakes","date":"2022-10-26","arxiv_id":"2210.14571","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-the-detection-of-diffusion-model#ran","syntology_url":"https://syntology.ai/paper/2210.14571","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.14571"}},"official":{"repos":["jonasricker/diffusion-model-deepfake-detection"],"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/black-box-model-explanations-and-the-human","slug":"black-box-model-explanations-and-the-human","title":"Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide Prediction","date":"2022-10-19","arxiv_id":"2210.10849","repositories_listed":2,"syntology":null},{"url":"/paper/region2vec-community-detection-on-spatial","slug":"region2vec-community-detection-on-spatial","title":"Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions","date":"2022-10-10","arxiv_id":"2210.08041","repositories_listed":2,"syntology":null},{"url":"/paper/omnigrok-grokking-beyond-algorithmic-data","slug":"omnigrok-grokking-beyond-algorithmic-data","title":"Omnigrok: Grokking Beyond Algorithmic Data","date":"2022-10-03","arxiv_id":"2210.01117","repositories_listed":2,"syntology":null},{"url":"/paper/fairness-in-face-presentation-attack","slug":"fairness-in-face-presentation-attack","title":"Fairness in Face Presentation Attack Detection","date":"2022-09-19","arxiv_id":"2209.09035","repositories_listed":2,"syntology":null},{"url":"/paper/fairdisco-fairer-ai-in-dermatology-via","slug":"fairdisco-fairer-ai-in-dermatology-via","title":"FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning","date":"2022-08-22","arxiv_id":"2208.10013","repositories_listed":2,"syntology":null},{"url":"/paper/multimodal-lecture-presentations-dataset","slug":"multimodal-lecture-presentations-dataset","title":"Multimodal Lecture Presentations Dataset: Understanding Multimodality in Educational Slides","date":"2022-08-17","arxiv_id":"2208.08080","repositories_listed":2,"syntology":null},{"url":"/paper/duet-cross-modal-semantic-grounding-for","slug":"duet-cross-modal-semantic-grounding-for","title":"DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning","date":"2022-07-04","arxiv_id":"2207.01328","repositories_listed":2,"syntology":null},{"url":"/paper/clip-art-contrastive-pre-training-for-fine-1","slug":"clip-art-contrastive-pre-training-for-fine-1","title":"CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification","date":"2022-04-29","arxiv_id":"2204.14244","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/clip-art-contrastive-pre-training-for-fine-1#ran","syntology_url":"https://syntology.ai/paper/2204.14244","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.14244"}},"official":{"repos":["KeremTurgutlu/clip_art","KeremTurgutlu/self_supervised"],"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/uncertainty-informed-deep-learning-models","slug":"uncertainty-informed-deep-learning-models","title":"Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology","date":"2022-04-09","arxiv_id":"2204.04516","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/uncertainty-informed-deep-learning-models#ran","syntology_url":"https://syntology.ai/paper/2204.04516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.04516"}},"official":{"repos":["jamesdolezal/biscuit","jamesdolezal/slideflow"],"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/the-moral-integrity-corpus-a-benchmark-for","slug":"the-moral-integrity-corpus-a-benchmark-for","title":"The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems","date":"2022-04-06","arxiv_id":"2204.03021","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/the-moral-integrity-corpus-a-benchmark-for#ran","syntology_url":"https://syntology.ai/paper/2204.03021","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.03021"}},"official":{"repos":["gt-salt/mic"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"url":"/paper/carca-context-and-attribute-aware-next-item","slug":"carca-context-and-attribute-aware-next-item","title":"CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention","date":"2022-04-04","arxiv_id":"2204.06519","repositories_listed":2,"syntology":null},{"url":"/paper/improving-adversarial-transferability-via","slug":"improving-adversarial-transferability-via","title":"Improving Adversarial Transferability via Neuron Attribution-Based Attacks","date":"2022-03-31","arxiv_id":"2204.00008","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/improving-adversarial-transferability-via#ran","syntology_url":"https://syntology.ai/paper/2204.00008","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00008"}},"official":{"repos":["jpzhang1810/naa"],"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/restoring-and-attributing-ancient-texts-using","slug":"restoring-and-attributing-ancient-texts-using","title":"Restoring and attributing ancient texts using deep neural networks","date":"2022-03-09","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/msdn-mutually-semantic-distillation-network","slug":"msdn-mutually-semantic-distillation-network","title":"MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning","date":"2022-03-07","arxiv_id":"2203.03137","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":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/msdn-mutually-semantic-distillation-network#ran","syntology_url":"https://syntology.ai/paper/2203.03137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03137"}},"official":{"repos":["shiming-chen/msdn"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/metaformer-a-unified-meta-framework-for-fine","slug":"metaformer-a-unified-meta-framework-for-fine","title":"MetaFormer: A Unified Meta Framework for Fine-Grained Recognition","date":"2022-03-05","arxiv_id":"2203.02751","repositories_listed":2,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"phrase":"9 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; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/metaformer-a-unified-meta-framework-for-fine#ran","syntology_url":"https://syntology.ai/paper/2203.02751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02751"}},"official":{"repos":["dqshuai/metaformer"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/false-clustering-rate-in-mixture-models","slug":"false-clustering-rate-in-mixture-models","title":"False membership rate control in mixture models","date":"2022-03-04","arxiv_id":"2203.02597","repositories_listed":2,"syntology":null},{"url":"/paper/attribute-descent-simulating-object-centric","slug":"attribute-descent-simulating-object-centric","title":"Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond","date":"2022-02-28","arxiv_id":"2202.14034","repositories_listed":2,"syntology":null},{"url":"/paper/dataset-condensation-with-contrastive-signals","slug":"dataset-condensation-with-contrastive-signals","title":"Dataset Condensation with Contrastive Signals","date":"2022-02-07","arxiv_id":"2202.02916","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dataset-condensation-with-contrastive-signals#ran","syntology_url":"https://syntology.ai/paper/2202.02916","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.02916"}},"official":{"repos":["saehyung-lee/dcc"],"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/attribute-based-progressive-fusion-network","slug":"attribute-based-progressive-fusion-network","title":"Attribute-Based Progressive Fusion Network for RGBT Tracking","date":"2022-01-26","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/a-latent-variable-model-for-intrinsic-probing-1","slug":"a-latent-variable-model-for-intrinsic-probing-1","title":"A Latent-Variable Model for Intrinsic Probing","date":"2022-01-20","arxiv_id":"2201.08214","repositories_listed":2,"syntology":null},{"url":"/paper/pedestrian-detection-domain-generalization","slug":"pedestrian-detection-domain-generalization","title":"Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond","date":"2022-01-10","arxiv_id":"2201.03176","repositories_listed":2,"syntology":null},{"url":"/paper/newsclaims-a-new-benchmark-for-claim","slug":"newsclaims-a-new-benchmark-for-claim","title":"NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge","date":"2021-12-16","arxiv_id":"2112.08544","repositories_listed":2,"syntology":null},{"url":"/paper/face-presentation-attack-detection-using","slug":"face-presentation-attack-detection-using","title":"FRT-PAD: Effective Presentation Attack Detection Driven by Face Related Task","date":"2021-11-22","arxiv_id":"2111.11046","repositories_listed":2,"syntology":null},{"url":"/paper/sparse-tensor-based-multiscale-representation","slug":"sparse-tensor-based-multiscale-representation","title":"Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression","date":"2021-11-20","arxiv_id":"2111.10633","repositories_listed":2,"syntology":null},{"url":"/paper/control-prefixes-for-text-generation","slug":"control-prefixes-for-text-generation","title":"Control Prefixes for Parameter-Efficient Text Generation","date":"2021-10-15","arxiv_id":"2110.08329","repositories_listed":2,"syntology":null},{"url":"/paper/on-the-pitfalls-of-analyzing-individual-1","slug":"on-the-pitfalls-of-analyzing-individual-1","title":"On the Pitfalls of Analyzing Individual Neurons in Language Models","date":"2021-10-14","arxiv_id":"2110.07483","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-the-pitfalls-of-analyzing-individual-1#ran","syntology_url":"https://syntology.ai/paper/2110.07483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.07483"}},"official":{"repos":["omerant/individual-neurons","technion-cs-nlp/individual-neurons-pitfalls"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-imagenet-how-to-discover-spurious-1","slug":"causal-imagenet-how-to-discover-spurious-1","title":"Salient ImageNet: How to discover spurious features in Deep Learning?","date":"2021-10-08","arxiv_id":"2110.04301","repositories_listed":2,"syntology":null},{"url":"/paper/generative-modeling-with-optimal-transport","slug":"generative-modeling-with-optimal-transport","title":"Generative Modeling with Optimal Transport Maps","date":"2021-10-06","arxiv_id":"2110.02999","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/generative-modeling-with-optimal-transport#ran","syntology_url":"https://syntology.ai/paper/2110.02999","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.02999"}},"official":{"repos":["LituRout/OptimalTransportModeling"],"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/attention-driven-graph-clustering-network","slug":"attention-driven-graph-clustering-network","title":"Attention-driven Graph Clustering Network","date":"2021-08-12","arxiv_id":"2108.05499","repositories_listed":2,"syntology":null},{"url":"/paper/multi-head-self-attention-via-vision","slug":"multi-head-self-attention-via-vision","title":"Multi-Head Self-Attention via Vision Transformer for Zero-Shot Learning","date":"2021-07-30","arxiv_id":"2108.00045","repositories_listed":2,"syntology":null},{"url":"/paper/separating-skills-and-concepts-for-novel-1","slug":"separating-skills-and-concepts-for-novel-1","title":"Separating Skills and Concepts for Novel Visual Question Answering","date":"2021-07-19","arxiv_id":"2107.09106","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/separating-skills-and-concepts-for-novel-1#ran","syntology_url":"https://syntology.ai/paper/2107.09106","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.09106"}},"official":{"repos":["SpencerWhitehead/novelvqa"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}}],"record_sha256":"6a1c078db4021d573879cc3a300e8b5fcb4776a7dbcadbbe9f3d180c474a1c5d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}