{"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/contrastive-learning/papers/11","list_of":"/task/contrastive-learning","task":"Contrastive 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":11,"pages_in_order":67,"rows_per_page":100,"rows":[1001,1100],"of":6661,"counts":{"archive_papers_tagged":6661,"with_a_code_link":3104,"where_syntology_ran_a_sample":920,"not_listed_spam_title":0,"listed":6661,"listed_where_code_ran":920,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":795,"every_run_a_failure_of_syntologys_instrument":125,"listed_with_a_run_with_no_instrument_failure":795,"listed_every_run_a_failure_of_syntologys_instrument":125,"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/contrastive-learning","prev":"/task/contrastive-learning/papers/10","next":"/task/contrastive-learning/papers/12","papers":[{"url":"/paper/self-supervised-adversarial-training-of","slug":"self-supervised-adversarial-training-of","title":"Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks","date":"2024-06-09","arxiv_id":"2406.05857","repositories_listed":1,"syntology":{"n":17,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":6,"n_honours":1,"n_violates":1,"n_no_contract":8,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 1 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/self-supervised-adversarial-training-of#ran","syntology_url":"https://syntology.ai/paper/2406.05857","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05857"}},"official":{"repos":["Bob-cheng/DepthModelHardening"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/separating-the-chirp-from-the-chat-self","slug":"separating-the-chirp-from-the-chat-self","title":"Separating the \"Chirp\" from the \"Chat\": Self-supervised Visual Grounding of Sound and Language","date":"2024-06-09","arxiv_id":"2406.05629","repositories_listed":1,"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":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/separating-the-chirp-from-the-chat-self#ran","syntology_url":"https://syntology.ai/paper/2406.05629","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05629"}},"official":{"repos":["mhamilton723/DenseAV"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/confidence-aware-contrastive-learning-for","slug":"confidence-aware-contrastive-learning-for","title":"Confidence-aware Contrastive Learning for Selective Classification","date":"2024-06-07","arxiv_id":"2406.04745","repositories_listed":1,"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":0,"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/confidence-aware-contrastive-learning-for#ran","syntology_url":"https://syntology.ai/paper/2406.04745","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04745"}},"official":{"repos":["lamda-bbo/CCL-SC"],"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/cplip-zero-shot-learning-for-histopathology","slug":"cplip-zero-shot-learning-for-histopathology","title":"CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment","date":"2024-06-07","arxiv_id":"2406.05205","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/cplip-zero-shot-learning-for-histopathology#ran","syntology_url":"https://syntology.ai/paper/2406.05205","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05205"}},"official":{"repos":["iyyakuttiiyappan/CPLIP"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/denoising-aware-contrastive-learning-for","slug":"denoising-aware-contrastive-learning-for","title":"Denoising-Aware Contrastive Learning for Noisy Time Series","date":"2024-06-07","arxiv_id":"2406.04627","repositories_listed":1,"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/denoising-aware-contrastive-learning-for#ran","syntology_url":"https://syntology.ai/paper/2406.04627","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04627"}},"official":{"repos":["betterzhou/DECL"],"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/dualtime-a-dual-adapter-multimodal-language","slug":"dualtime-a-dual-adapter-multimodal-language","title":"MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning","date":"2024-06-07","arxiv_id":"2406.06620","repositories_listed":1,"syntology":null},{"url":"/paper/ma-avt-modality-alignment-for-parameter","slug":"ma-avt-modality-alignment-for-parameter","title":"MA-AVT: Modality Alignment for Parameter-Efficient Audio-Visual Transformers","date":"2024-06-07","arxiv_id":"2406.04930","repositories_listed":1,"syntology":null},{"url":"/paper/skill-aware-mutual-information-optimisation","slug":"skill-aware-mutual-information-optimisation","title":"Skill-aware Mutual Information Optimisation for Generalisation in Reinforcement Learning","date":"2024-06-07","arxiv_id":"2406.04815","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":1,"n_ran_checked":5,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":3,"n_pointer_only":7,"phrase":"7 ran (of which 1 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 2 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/skill-aware-mutual-information-optimisation#ran","syntology_url":"https://syntology.ai/paper/2406.04815","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04815"}},"official":{"repos":["uoe-agents/sami"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":1,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/jigmark-a-black-box-approach-for-enhancing","slug":"jigmark-a-black-box-approach-for-enhancing","title":"JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model Edits","date":"2024-06-06","arxiv_id":"2406.03720","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":3,"n_no_contract":5,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 3 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/jigmark-a-black-box-approach-for-enhancing#ran","syntology_url":"https://syntology.ai/paper/2406.03720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.03720"}},"official":{"repos":["pmzzs/JigMark"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/low-rank-similarity-mining-for-multimodal","slug":"low-rank-similarity-mining-for-multimodal","title":"Low-Rank Similarity Mining for Multimodal Dataset Distillation","date":"2024-06-06","arxiv_id":"2406.03793","repositories_listed":1,"syntology":{"n":16,"n_ran":12,"n_constructed":1,"n_ran_checked":11,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":10,"n_pointer_only":4,"phrase":"12 ran (of which 1 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 1 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/low-rank-similarity-mining-for-multimodal#ran","syntology_url":"https://syntology.ai/paper/2406.03793","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.03793"}},"official":{"repos":["silicx/lors_distill"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":1,"n_ran_no_instrument_failure":11,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-user-retrieval-integration-towards","slug":"exploring-user-retrieval-integration-towards","title":"Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation","date":"2024-06-05","arxiv_id":"2406.03085","repositories_listed":1,"syntology":null},{"url":"/paper/mind-s-eye-image-recognition-by-eeg-via","slug":"mind-s-eye-image-recognition-by-eeg-via","title":"Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learning","date":"2024-06-05","arxiv_id":"2406.16910","repositories_listed":1,"syntology":null},{"url":"/paper/mmcl-boosting-deformable-detr-based-detectors","slug":"mmcl-boosting-deformable-detr-based-detectors","title":"MMCL: Boosting Deformable DETR-Based Detectors with Multi-Class Min-Margin Contrastive Learning for Superior Prohibited Item Detection","date":"2024-06-05","arxiv_id":"2406.03176","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-multi-scale-contrastive-knowledge","slug":"multi-task-multi-scale-contrastive-knowledge","title":"Multi-Task Multi-Scale Contrastive Knowledge Distillation for Efficient Medical Image Segmentation","date":"2024-06-05","arxiv_id":"2406.03173","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-skeleton-action","slug":"self-supervised-skeleton-action","title":"Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond","date":"2024-06-05","arxiv_id":"2406.02978","repositories_listed":1,"syntology":null},{"url":"/paper/dda-dimensionality-driven-augmentation-search","slug":"dda-dimensionality-driven-augmentation-search","title":"DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery","date":"2024-06-03","arxiv_id":"2406.00907","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-contrastive-analysis-for-salient","slug":"unsupervised-contrastive-analysis-for-salient","title":"Unsupervised Contrastive Analysis for Salient Pattern Detection using Conditional Diffusion Models","date":"2024-06-02","arxiv_id":"2406.00772","repositories_listed":1,"syntology":null},{"url":"/paper/improving-paratope-and-epitope-prediction-by","slug":"improving-paratope-and-epitope-prediction-by","title":"Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation","date":"2024-05-31","arxiv_id":"2405.20668","repositories_listed":1,"syntology":null},{"url":"/paper/link-learning-joint-representations-of-design","slug":"link-learning-joint-representations-of-design","title":"LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis","date":"2024-05-31","arxiv_id":"2405.20592","repositories_listed":1,"syntology":null},{"url":"/paper/popularity-aware-alignment-and-contrast-for","slug":"popularity-aware-alignment-and-contrast-for","title":"Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias","date":"2024-05-31","arxiv_id":"2405.20718","repositories_listed":1,"syntology":null},{"url":"/paper/vision-language-meets-the-skeleton","slug":"vision-language-meets-the-skeleton","title":"Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learning","date":"2024-05-31","arxiv_id":"2405.20606","repositories_listed":1,"syntology":null},{"url":"/paper/medication-recommendation-via-dual-molecular","slug":"medication-recommendation-via-dual-molecular","title":"Medication Recommendation via Dual Molecular Modalities and Multi-Step Enhancement","date":"2024-05-30","arxiv_id":"2405.20358","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-guided-soft-contrastive-learning","slug":"multi-label-guided-soft-contrastive-learning","title":"Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining","date":"2024-05-30","arxiv_id":"2405.20462","repositories_listed":1,"syntology":null},{"url":"/paper/towards-ontology-enhanced-representation","slug":"towards-ontology-enhanced-representation","title":"Towards Ontology-Enhanced Representation Learning for Large Language Models","date":"2024-05-30","arxiv_id":"2405.20527","repositories_listed":1,"syntology":null},{"url":"/paper/video-language-critic-transferable-reward","slug":"video-language-critic-transferable-reward","title":"Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics","date":"2024-05-30","arxiv_id":"2405.19988","repositories_listed":1,"syntology":null},{"url":"/paper/encoding-hierarchical-schema-via-concept-flow","slug":"encoding-hierarchical-schema-via-concept-flow","title":"Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection","date":"2024-05-29","arxiv_id":"2405.18974","repositories_listed":1,"syntology":null},{"url":"/paper/a-vlogger-augmented-graph-neural-network","slug":"a-vlogger-augmented-graph-neural-network","title":"A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation","date":"2024-05-28","arxiv_id":"2405.18260","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-mini-batch-and-asymptotic-analysis","slug":"bridging-mini-batch-and-asymptotic-analysis","title":"Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses","date":"2024-05-28","arxiv_id":"2405.18045","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"9 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bridging-mini-batch-and-asymptotic-analysis#ran","syntology_url":"https://syntology.ai/paper/2405.18045","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.18045"}},"official":{"repos":["pakoromilas/dhel-kcl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ldmol-text-conditioned-molecule-diffusion","slug":"ldmol-text-conditioned-molecule-diffusion","title":"LDMol: Text-to-Molecule Diffusion Model with Structurally Informative Latent Space","date":"2024-05-28","arxiv_id":"2405.17829","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":2,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 ran (of which 2 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ldmol-text-conditioned-molecule-diffusion#ran","syntology_url":"https://syntology.ai/paper/2405.17829","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17829"}},"official":{"repos":["jinhojsk515/ldmol"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/ov-dquo-open-vocabulary-detr-with-denoising","slug":"ov-dquo-open-vocabulary-detr-with-denoising","title":"OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects Supervision","date":"2024-05-28","arxiv_id":"2405.17913","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ov-dquo-open-vocabulary-detr-with-denoising#ran","syntology_url":"https://syntology.ai/paper/2405.17913","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17913"}},"official":{"repos":["xiaomoguhz/ov-dquo"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/relational-self-supervised-distillation-with","slug":"relational-self-supervised-distillation-with","title":"Relational Self-supervised Distillation with Compact Descriptors for Image Copy Detection","date":"2024-05-28","arxiv_id":"2405.17928","repositories_listed":1,"syntology":null},{"url":"/paper/sleepfm-multi-modal-representation-learning","slug":"sleepfm-multi-modal-representation-learning","title":"SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals","date":"2024-05-28","arxiv_id":"2405.17766","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sleepfm-multi-modal-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2405.17766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17766"}},"official":{"repos":["rthapa84/sleepfm-codebase"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/sslchange-a-self-supervised-change-detection","slug":"sslchange-a-self-supervised-change-detection","title":"SSLChange: A Self-supervised Change Detection Framework Based on Domain Adaptation","date":"2024-05-28","arxiv_id":"2405.18224","repositories_listed":1,"syntology":null},{"url":"/paper/automatically-generating-numerous-context","slug":"automatically-generating-numerous-context","title":"Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse Granularity","date":"2024-05-26","arxiv_id":"2405.16579","repositories_listed":1,"syntology":null},{"url":"/paper/improving-multi-lingual-alignment-through","slug":"improving-multi-lingual-alignment-through","title":"Improving Multi-lingual Alignment Through Soft Contrastive Learning","date":"2024-05-25","arxiv_id":"2405.16155","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":10,"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) · 1 unverified","sample_list":"/paper/improving-multi-lingual-alignment-through#ran","syntology_url":"https://syntology.ai/paper/2405.16155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16155"}},"official":{"repos":["yai12xlinq-b/imascl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/paths-of-a-million-people-extracting-life","slug":"paths-of-a-million-people-extracting-life","title":"Paths of A Million People: Extracting Life Trajectories from Wikipedia","date":"2024-05-25","arxiv_id":"2406.00032","repositories_listed":1,"syntology":null},{"url":"/paper/usd-unsupervised-soft-contrastive-learning","slug":"usd-unsupervised-soft-contrastive-learning","title":"USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time Series","date":"2024-05-25","arxiv_id":"2405.16258","repositories_listed":1,"syntology":null},{"url":"/paper/nuwats-mending-every-incomplete-time-series","slug":"nuwats-mending-every-incomplete-time-series","title":"NuwaTS: a Foundation Model Mending Every Incomplete Time Series","date":"2024-05-24","arxiv_id":"2405.15317","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/nuwats-mending-every-incomplete-time-series#ran","syntology_url":"https://syntology.ai/paper/2405.15317","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.15317"}},"official":{"repos":["chengyui/nuwats"],"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/rethinking-class-incremental-learning-from-a","slug":"rethinking-class-incremental-learning-from-a","title":"Rethinking Class-Incremental Learning from a Dynamic Imbalanced Learning Perspective","date":"2024-05-24","arxiv_id":"2405.15157","repositories_listed":1,"syntology":null},{"url":"/paper/self-contrastive-weakly-supervised-learning","slug":"self-contrastive-weakly-supervised-learning","title":"Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images","date":"2024-05-24","arxiv_id":"2405.15264","repositories_listed":1,"syntology":null},{"url":"/paper/slide-a-framework-integrating-small-and-large","slug":"slide-a-framework-integrating-small-and-large","title":"SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation","date":"2024-05-24","arxiv_id":"2405.15924","repositories_listed":1,"syntology":null},{"url":"/paper/combining-denoising-autoencoders-with","slug":"combining-denoising-autoencoders-with","title":"Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer Models","date":"2024-05-23","arxiv_id":"2405.14437","repositories_listed":1,"syntology":null},{"url":"/paper/harmony-a-joint-self-supervised-and-weakly","slug":"harmony-a-joint-self-supervised-and-weakly","title":"Harmony: A Joint Self-Supervised and Weakly-Supervised Framework for Learning General Purpose Visual Representations","date":"2024-05-23","arxiv_id":"2405.14239","repositories_listed":1,"syntology":null},{"url":"/paper/improved-canonicalization-for-model-agnostic","slug":"improved-canonicalization-for-model-agnostic","title":"Improved Canonicalization for Model Agnostic Equivariance","date":"2024-05-23","arxiv_id":"2405.14089","repositories_listed":1,"syntology":null},{"url":"/paper/improving-gloss-free-sign-language","slug":"improving-gloss-free-sign-language","title":"Improving Gloss-free Sign Language Translation by Reducing Representation Density","date":"2024-05-23","arxiv_id":"2405.14312","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":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/improving-gloss-free-sign-language#ran","syntology_url":"https://syntology.ai/paper/2405.14312","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14312"}},"official":{"repos":["jinhuiye/signcl"],"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/trojanrag-retrieval-augmented-generation-can","slug":"trojanrag-retrieval-augmented-generation-can","title":"TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models","date":"2024-05-22","arxiv_id":"2405.13401","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-interpretable-information","slug":"efficient-and-interpretable-information","title":"Efficient and Interpretable Information Retrieval for Product Question Answering with Heterogeneous Data","date":"2024-05-21","arxiv_id":"2405.13173","repositories_listed":1,"syntology":null},{"url":"/paper/casegnn-graph-contrastive-learning-for-legal","slug":"casegnn-graph-contrastive-learning-for-legal","title":"CaseGNN++: Graph Contrastive Learning for Legal Case Retrieval with Graph Augmentation","date":"2024-05-20","arxiv_id":"2405.11791","repositories_listed":1,"syntology":null},{"url":"/paper/datr-unsupervised-domain-adaptive-detection","slug":"datr-unsupervised-domain-adaptive-detection","title":"DATR: Unsupervised Domain Adaptive Detection Transformer with Dataset-Level Adaptation and Prototypical Alignment","date":"2024-05-20","arxiv_id":"2405.11765","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-model-stealing-attacks-against","slug":"efficient-model-stealing-attacks-against","title":"Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks","date":"2024-05-20","arxiv_id":"2405.12295","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-user-fatigue-for-sequential","slug":"modeling-user-fatigue-for-sequential","title":"Modeling User Fatigue for Sequential Recommendation","date":"2024-05-20","arxiv_id":"2405.11764","repositories_listed":1,"syntology":null},{"url":"/paper/transcriptomics-guided-slide-representation","slug":"transcriptomics-guided-slide-representation","title":"Transcriptomics-guided Slide Representation Learning in Computational Pathology","date":"2024-05-19","arxiv_id":"2405.11618","repositories_listed":1,"syntology":{"n":17,"n_ran":15,"n_constructed":5,"n_ran_checked":14,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":17,"phrase":"15 ran (of which 5 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/transcriptomics-guided-slide-representation#ran","syntology_url":"https://syntology.ai/paper/2405.11618","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.11618"}},"official":{"repos":["mahmoodlab/tangle"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":5,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/sebot-structural-entropy-guided-multi-view","slug":"sebot-structural-entropy-guided-multi-view","title":"SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detection","date":"2024-05-18","arxiv_id":"2405.11225","repositories_listed":1,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":9,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":4,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sebot-structural-entropy-guided-multi-view#ran","syntology_url":"https://syntology.ai/paper/2405.11225","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.11225"}},"official":{"repos":["846468230/sebot"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/in-context-contrastive-learning-for-event","slug":"in-context-contrastive-learning-for-event","title":"In-context Contrastive Learning for Event Causality Identification","date":"2024-05-17","arxiv_id":"2405.10512","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-semantics-in-multimodal-chain-of","slug":"enhancing-semantics-in-multimodal-chain-of","title":"Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling","date":"2024-05-16","arxiv_id":"2405.09848","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-based-contrastive-learning-for","slug":"diffusion-based-contrastive-learning-for","title":"Diffusion-based Contrastive Learning for Sequential Recommendation","date":"2024-05-15","arxiv_id":"2405.09369","repositories_listed":1,"syntology":null},{"url":"/paper/factual-serialization-enhancement-a-key","slug":"factual-serialization-enhancement-a-key","title":"Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation","date":"2024-05-15","arxiv_id":"2405.09586","repositories_listed":1,"syntology":null},{"url":"/paper/time-equivariant-contrastive-learning-for","slug":"time-equivariant-contrastive-learning-for","title":"Learning Temporally Equivariance for Degenerative Disease Progression in OCT by Predicting Future Representations","date":"2024-05-15","arxiv_id":"2405.09404","repositories_listed":1,"syntology":null},{"url":"/paper/dual-level-hypergraph-contrastive-learning","slug":"dual-level-hypergraph-contrastive-learning","title":"Dual-level Hypergraph Contrastive Learning with Adaptive Temperature Enhancement","date":"2024-05-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficient-vision-language-pre-training-by","slug":"efficient-vision-language-pre-training-by","title":"Efficient Vision-Language Pre-training by Cluster Masking","date":"2024-05-14","arxiv_id":"2405.08815","repositories_listed":1,"syntology":{"n":17,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":3,"n_no_contract":8,"n_pointer_only":17,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 3 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/efficient-vision-language-pre-training-by#ran","syntology_url":"https://syntology.ai/paper/2405.08815","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.08815"}},"official":{"repos":["zi-hao-wei/efficient-vision-language-pre-training-by-cluster-masking"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/self-distillation-improves-dna-sequence","slug":"self-distillation-improves-dna-sequence","title":"Self-Distillation Improves DNA Sequence Inference","date":"2024-05-14","arxiv_id":"2405.08538","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-contrastive-learning-unveils","slug":"self-supervised-contrastive-learning-unveils","title":"Self-supervised contrastive learning unveils cortical folding pattern linked to prematurity","date":"2024-05-14","arxiv_id":"2405.08397","repositories_listed":1,"syntology":null},{"url":"/paper/t3rd-test-time-training-for-rumor-detection","slug":"t3rd-test-time-training-for-rumor-detection","title":"T3RD: Test-Time Training for Rumor Detection on Social Media","date":"2024-05-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-supervised-information-enhanced-multi","slug":"a-supervised-information-enhanced-multi","title":"A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition","date":"2024-05-12","arxiv_id":"2405.07260","repositories_listed":1,"syntology":null},{"url":"/paper/novel-class-discovery-for-ultra-fine-grained","slug":"novel-class-discovery-for-ultra-fine-grained","title":"Novel Class Discovery for Ultra-Fine-Grained Visual Categorization","date":"2024-05-10","arxiv_id":"2405.06283","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":13,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/novel-class-discovery-for-ultra-fine-grained#ran","syntology_url":"https://syntology.ai/paper/2405.06283","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.06283"}},"official":{"repos":["ssdut-caiyq/ufg-ncd"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pclmix-weakly-supervised-medical-image","slug":"pclmix-weakly-supervised-medical-image","title":"PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation","date":"2024-05-10","arxiv_id":"2405.06288","repositories_listed":1,"syntology":null},{"url":"/paper/dtclmapper-dual-temporal-consistent-learning","slug":"dtclmapper-dual-temporal-consistent-learning","title":"DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction","date":"2024-05-09","arxiv_id":"2405.05518","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-pre-training-with-symmetric","slug":"self-supervised-pre-training-with-symmetric","title":"Self-Supervised Pre-training with Symmetric Superimposition Modeling for Scene Text Recognition","date":"2024-05-09","arxiv_id":"2405.05841","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"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) · 2 unverified","sample_list":"/paper/self-supervised-pre-training-with-symmetric#ran","syntology_url":"https://syntology.ai/paper/2405.05841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.05841"}},"official":{"repos":["faltingsa/ssm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-margin-loss-proposal-and-application-in","slug":"multi-margin-loss-proposal-and-application-in","title":"Multi-Margin Cosine Loss: Proposal and Application in Recommender Systems","date":"2024-05-07","arxiv_id":"2405.04614","repositories_listed":1,"syntology":null},{"url":"/paper/refining-joint-text-and-source-code","slug":"refining-joint-text-and-source-code","title":"Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuning","date":"2024-05-07","arxiv_id":"2405.04126","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-breast-cancer","slug":"classification-of-breast-cancer","title":"Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning Method","date":"2024-05-06","arxiv_id":"2405.03642","repositories_listed":1,"syntology":null},{"url":"/paper/geocontrastnet-contrastive-key-value-edge","slug":"geocontrastnet-contrastive-key-value-edge","title":"GeoContrastNet: Contrastive Key-Value Edge Learning for Language-Agnostic Document Understanding","date":"2024-05-06","arxiv_id":"2405.03104","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-micro-gesture-recognition-for","slug":"enhancing-micro-gesture-recognition-for","title":"Enhancing Micro Gesture Recognition for Emotion Understanding via Context-aware Visual-Text Contrastive Learning","date":"2024-05-03","arxiv_id":"2405.01885","repositories_listed":1,"syntology":null},{"url":"/paper/community-invariant-graph-contrastive","slug":"community-invariant-graph-contrastive","title":"Community-Invariant Graph Contrastive Learning","date":"2024-05-02","arxiv_id":"2405.01350","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":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) · 0 unverified","sample_list":"/paper/community-invariant-graph-contrastive#ran","syntology_url":"https://syntology.ai/paper/2405.01350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.01350"}},"official":{"repos":["shiyintan/ci-gcl"],"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/a-self-explaining-neural-architecture-for","slug":"a-self-explaining-neural-architecture-for","title":"A Self-explaining Neural Architecture for Generalizable Concept Learning","date":"2024-05-01","arxiv_id":"2405.00349","repositories_listed":1,"syntology":null},{"url":"/paper/semipl-a-semi-supervised-method-for-event","slug":"semipl-a-semi-supervised-method-for-event","title":"SemiPL: A Semi-supervised Method for Event Sound Source Localization","date":"2024-04-30","arxiv_id":"2404.19615","repositories_listed":1,"syntology":null},{"url":"/paper/stablept-towards-stable-prompting-for-few","slug":"stablept-towards-stable-prompting-for-few","title":"StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation","date":"2024-04-30","arxiv_id":"2404.19335","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/stablept-towards-stable-prompting-for-few#ran","syntology_url":"https://syntology.ai/paper/2404.19335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.19335"}},"official":{"repos":["lccc0528/stable"],"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/conpro-learning-severity-representation-for","slug":"conpro-learning-severity-representation-for","title":"ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization","date":"2024-04-29","arxiv_id":"2404.18831","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"2 ran (of which 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) · 2 unverified","sample_list":"/paper/conpro-learning-severity-representation-for#ran","syntology_url":"https://syntology.ai/paper/2404.18831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.18831"}},"official":{"repos":["hong7cong/conpro"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mm-tts-a-unified-framework-for-multimodal","slug":"mm-tts-a-unified-framework-for-multimodal","title":"UMETTS: A Unified Framework for Emotional Text-to-Speech Synthesis with Multimodal Prompts","date":"2024-04-29","arxiv_id":"2404.18398","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mm-tts-a-unified-framework-for-multimodal#ran","syntology_url":"https://syntology.ai/paper/2404.18398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.18398"}},"official":{"repos":["kttrcdl/umetts"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/source-free-domain-adaptation-of-weakly","slug":"source-free-domain-adaptation-of-weakly","title":"Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology","date":"2024-04-29","arxiv_id":"2404.19113","repositories_listed":1,"syntology":null},{"url":"/paper/deep-boosting-learning-a-brand-new","slug":"deep-boosting-learning-a-brand-new","title":"Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching","date":"2024-04-28","arxiv_id":"2404.18114","repositories_listed":1,"syntology":null},{"url":"/paper/retrieval-oriented-knowledge-for-click","slug":"retrieval-oriented-knowledge-for-click","title":"Retrieval-Oriented Knowledge for Click-Through Rate Prediction","date":"2024-04-28","arxiv_id":"2404.18304","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-cross-modal-neighbor","slug":"leveraging-cross-modal-neighbor","title":"Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification","date":"2024-04-27","arxiv_id":"2404.17753","repositories_listed":1,"syntology":{"n":20,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":9,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":20,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/leveraging-cross-modal-neighbor#ran","syntology_url":"https://syntology.ai/paper/2404.17753","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.17753"}},"official":{"repos":["ycaigogogo/cvpr24-coder"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/promptcl-improving-event-representation-via","slug":"promptcl-improving-event-representation-via","title":"PromptCL: Improving Event Representation via Prompt Template and Contrastive Learning","date":"2024-04-27","arxiv_id":"2404.17877","repositories_listed":1,"syntology":null},{"url":"/paper/fedstyle-style-based-federated-learning","slug":"fedstyle-style-based-federated-learning","title":"FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art Commissions","date":"2024-04-25","arxiv_id":"2404.16336","repositories_listed":1,"syntology":null},{"url":"/paper/catlip-clip-level-visual-recognition-accuracy","slug":"catlip-clip-level-visual-recognition-accuracy","title":"CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data","date":"2024-04-24","arxiv_id":"2404.15653","repositories_listed":1,"syntology":null},{"url":"/paper/clad-robust-audio-deepfake-detection-against","slug":"clad-robust-audio-deepfake-detection-against","title":"CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning","date":"2024-04-24","arxiv_id":"2404.15854","repositories_listed":1,"syntology":null},{"url":"/paper/ckd-contrastive-knowledge-distillation-from-a","slug":"ckd-contrastive-knowledge-distillation-from-a","title":"CKD: Contrastive Knowledge Distillation from A Sample-wise Perspective","date":"2024-04-22","arxiv_id":"2404.14109","repositories_listed":1,"syntology":null},{"url":"/paper/multi-level-sequence-denoising-with-cross","slug":"multi-level-sequence-denoising-with-cross","title":"Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential Recommendation","date":"2024-04-22","arxiv_id":"2404.13878","repositories_listed":1,"syntology":null},{"url":"/paper/preference-fine-tuning-of-llms-should","slug":"preference-fine-tuning-of-llms-should","title":"Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data","date":"2024-04-22","arxiv_id":"2404.14367","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/preference-fine-tuning-of-llms-should#ran","syntology_url":"https://syntology.ai/paper/2404.14367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14367"}},"official":{"repos":["Asap7772/understanding-rlhf"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/tavgbench-benchmarking-text-to-audible-video","slug":"tavgbench-benchmarking-text-to-audible-video","title":"TAVGBench: Benchmarking Text to Audible-Video Generation","date":"2024-04-22","arxiv_id":"2404.14381","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":5,"n_pointer_only":11,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 2 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tavgbench-benchmarking-text-to-audible-video#ran","syntology_url":"https://syntology.ai/paper/2404.14381","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14381"}},"official":{"repos":["opennlplab/tavgbench"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/artnerf-a-stylized-neural-field-for-3d-aware","slug":"artnerf-a-stylized-neural-field-for-3d-aware","title":"ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis","date":"2024-04-21","arxiv_id":"2404.13711","repositories_listed":1,"syntology":null},{"url":"/paper/chatretriever-adapting-large-language-models","slug":"chatretriever-adapting-large-language-models","title":"ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval","date":"2024-04-21","arxiv_id":"2404.13556","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chatretriever-adapting-large-language-models#ran","syntology_url":"https://syntology.ai/paper/2404.13556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.13556"}},"official":{"repos":["kyriemao/chatretriever"],"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/auto-formula-recommend-formulas-in","slug":"auto-formula-recommend-formulas-in","title":"Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations","date":"2024-04-19","arxiv_id":"2404.12608","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-medical-phrase-grounding-with-off","slug":"zero-shot-medical-phrase-grounding-with-off","title":"Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models","date":"2024-04-19","arxiv_id":"2404.12920","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/zero-shot-medical-phrase-grounding-with-off#ran","syntology_url":"https://syntology.ai/paper/2404.12920","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.12920"}},"official":{"repos":["vios-s/ldm-phrase-grounding"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/blind-localization-and-clustering-of","slug":"blind-localization-and-clustering-of","title":"Blind Localization and Clustering of Anomalies in Textures","date":"2024-04-18","arxiv_id":"2404.12246","repositories_listed":1,"syntology":null},{"url":"/paper/harnessing-joint-rain-detail-aware","slug":"harnessing-joint-rain-detail-aware","title":"Harnessing Joint Rain-/Detail-aware Representations to Eliminate Intricate Rains","date":"2024-04-18","arxiv_id":"2404.12091","repositories_listed":1,"syntology":null},{"url":"/paper/observation-analysis-and-solution-exploring","slug":"observation-analysis-and-solution-exploring","title":"An Experimental Study on Exploring Strong Lightweight Vision Transformers via Masked Image Modeling Pre-Training","date":"2024-04-18","arxiv_id":"2404.12210","repositories_listed":1,"syntology":null},{"url":"/paper/when-llms-are-unfit-use-fastfit-fast-and","slug":"when-llms-are-unfit-use-fastfit-fast-and","title":"When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classes","date":"2024-04-18","arxiv_id":"2404.12365","repositories_listed":1,"syntology":null},{"url":"/paper/dacad-domain-adaptation-contrastive-learning","slug":"dacad-domain-adaptation-contrastive-learning","title":"DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series","date":"2024-04-17","arxiv_id":"2404.11269","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dacad-domain-adaptation-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2404.11269","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.11269"}},"official":{"repos":["zamanzadeh/DACAD"],"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"]}}}],"record_sha256":"45d4f8ff7fd2215ff005726a7cd31e8943317b1a2df38dcd46199701f8ee136d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}