{"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":"/method/contrastive-learning/papers/13","list_of":"/method/contrastive-learning","method":"Contrastive Learning","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":13,"pages_in_order":51,"rows_per_page":100,"rows":[1201,1300],"of":5057,"counts":{"archive_papers_tagged":5057,"with_a_code_link":2344,"where_syntology_ran_a_sample":654,"not_listed_spam_title":0,"listed":5057,"listed_where_code_ran":654,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":568,"every_run_a_failure_of_syntologys_instrument":86,"listed_with_a_run_with_no_instrument_failure":568,"listed_every_run_a_failure_of_syntologys_instrument":86,"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":"/method/contrastive-learning","prev":"/method/contrastive-learning/papers/12","next":"/method/contrastive-learning/papers/14","papers":[{"paper":"/paper/from-real-to-cloned-singer-identification","slug":"from-real-to-cloned-singer-identification","title":"From Real to Cloned Singer Identification","date":"2024-07-11","arxiv_id":"2407.08647","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["deezer/real-cloned-singer-id"],"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"]}}},{"paper":null,"slug":"hierarchical-consensus-based-multi-agent","title":"Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks","date":"2024-07-11","arxiv_id":"2407.08164","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-contrastive-learning-for-spatial","slug":"multimodal-contrastive-learning-for-spatial","title":"Multimodal contrastive learning for spatial gene expression prediction using histology images","date":"2024-07-11","arxiv_id":"2407.08216","n_code_links":1,"syntology":{"ran":8,"of":9,"n_ran_checked":7,"n_instrument":1,"unverified":1,"pointer_only":9,"phrase":"8 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; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["shizhiceng/mclstexp"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"cosmoclip-generalizing-large-vision-language","title":"CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging","date":"2024-07-10","arxiv_id":"2407.07315","n_code_links":0,"syntology":null},{"paper":null,"slug":"ea-vtr-event-aware-video-text-retrieval","title":"EA-VTR: Event-Aware Video-Text Retrieval","date":"2024-07-10","arxiv_id":"2407.07478","n_code_links":0,"syntology":null},{"paper":"/paper/tip-tabular-image-pre-training-for-multimodal","slug":"tip-tabular-image-pre-training-for-multimodal","title":"TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete Data","date":"2024-07-10","arxiv_id":"2407.07582","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":1,"n_instrument":1,"unverified":2,"pointer_only":1,"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","official":{"repos":["siyi-wind/tip"],"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"]}}},{"paper":null,"slug":"cormult-a-semi-supervised-modality","title":"CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis","date":"2024-07-09","arxiv_id":"2407.07046","n_code_links":0,"syntology":null},{"paper":"/paper/htd-mamba-efficient-hyperspectral-target","slug":"htd-mamba-efficient-hyperspectral-target","title":"HTD-Mamba: Efficient Hyperspectral Target Detection with Pyramid State Space Model","date":"2024-07-09","arxiv_id":"2407.06841","n_code_links":1,"syntology":null},{"paper":"/paper/ittakestwo-leveraging-peer-representations","slug":"ittakestwo-leveraging-peer-representations","title":"ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation","date":"2024-07-09","arxiv_id":"2407.07171","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["yyliu01/it2"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/4d-contrastive-superflows-are-dense-3d","slug":"4d-contrastive-superflows-are-dense-3d","title":"4D Contrastive Superflows are Dense 3D Representation Learners","date":"2024-07-08","arxiv_id":"2407.06190","n_code_links":1,"syntology":null},{"paper":"/paper/an-accurate-detection-is-not-all-you-need-to","slug":"an-accurate-detection-is-not-all-you-need-to","title":"An accurate detection is not all you need to combat label noise in web-noisy datasets","date":"2024-07-08","arxiv_id":"2407.05528","n_code_links":1,"syntology":null},{"paper":"/paper/bringing-masked-autoencoders-explicit","slug":"bringing-masked-autoencoders-explicit","title":"Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning","date":"2024-07-08","arxiv_id":"2407.05862","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":5,"n_instrument":3,"unverified":0,"pointer_only":6,"phrase":"8 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["amazingren/point-cmae"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"hycir-boosting-zero-shot-composed-image","title":"HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels","date":"2024-07-08","arxiv_id":"2407.05795","n_code_links":0,"syntology":null},{"paper":"/paper/poisson-ordinal-network-for-gleason-group","slug":"poisson-ordinal-network-for-gleason-group","title":"Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI","date":"2024-07-08","arxiv_id":"2407.05796","n_code_links":1,"syntology":null},{"paper":null,"slug":"sequential-contrastive-audio-visual-learning","title":"Sequential Contrastive Audio-Visual Learning","date":"2024-07-08","arxiv_id":"2407.05782","n_code_links":0,"syntology":null},{"paper":"/paper/tile-compression-and-embeddings-for-multi","slug":"tile-compression-and-embeddings-for-multi","title":"Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024","date":"2024-07-08","arxiv_id":"2407.06326","n_code_links":1,"syntology":null},{"paper":"/paper/training-free-cryoet-tomogram-segmentation","slug":"training-free-cryoet-tomogram-segmentation","title":"Training-free CryoET Tomogram Segmentation","date":"2024-07-08","arxiv_id":"2407.06833","n_code_links":1,"syntology":null},{"paper":"/paper/clamp-vit-contrastive-data-free-learning-for","slug":"clamp-vit-contrastive-data-free-learning-for","title":"CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs","date":"2024-07-07","arxiv_id":"2407.05266","n_code_links":1,"syntology":{"ran":2,"of":6,"n_ran_checked":1,"n_instrument":1,"unverified":4,"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) · 4 unverified","official":{"repos":["georgia-tech-synergy-lab/clamp-vit"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/deep-probability-aggregation-clustering","slug":"deep-probability-aggregation-clustering","title":"Deep Online Probability Aggregation Clustering","date":"2024-07-07","arxiv_id":"2407.05246","n_code_links":1,"syntology":{"ran":7,"of":10,"n_ran_checked":7,"n_instrument":0,"unverified":3,"pointer_only":10,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["aomandechenai/deep-probability-aggregation-clustering"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/language-models-encode-collaborative-signals","slug":"language-models-encode-collaborative-signals","title":"Language Representations Can be What Recommenders Need: Findings and Potentials","date":"2024-07-07","arxiv_id":"2407.05441","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"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","official":{"repos":["lehengthu/alpharec"],"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"]}}},{"paper":"/paper/online-drift-detection-with-maximum-concept","slug":"online-drift-detection-with-maximum-concept","title":"Online Drift Detection with Maximum Concept Discrepancy","date":"2024-07-07","arxiv_id":"2407.05375","n_code_links":1,"syntology":null},{"paper":"/paper/self-paced-sample-selection-for-barely","slug":"self-paced-sample-selection-for-barely","title":"Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation","date":"2024-07-07","arxiv_id":"2407.05248","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-new-brain-network-construction-paradigm-for","title":"A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-based Graph Contrastive Learning","date":"2024-07-06","arxiv_id":"2407.18329","n_code_links":0,"syntology":null},{"paper":"/paper/consistency-and-discrepancy-based-contrastive","slug":"consistency-and-discrepancy-based-contrastive","title":"Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations","date":"2024-07-06","arxiv_id":"2407.05126","n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-lingual-word-alignment-for-asean","title":"Cross-Lingual Word Alignment for ASEAN Languages with Contrastive Learning","date":"2024-07-06","arxiv_id":"2407.05054","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-solution-for-language-enhanced-image-new","title":"The Solution for Language-Enhanced Image New Category Discovery","date":"2024-07-06","arxiv_id":"2407.04994","n_code_links":0,"syntology":null},{"paper":null,"slug":"trace-transformer-based-attribution-using","title":"TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs","date":"2024-07-06","arxiv_id":"2407.04981","n_code_links":0,"syntology":null},{"paper":"/paper/zero-shot-object-counting-with-good-exemplars","slug":"zero-shot-object-counting-with-good-exemplars","title":"Zero-shot Object Counting with Good Exemplars","date":"2024-07-06","arxiv_id":"2407.04948","n_code_links":1,"syntology":null},{"paper":"/paper/an-interactive-multi-modal-query-answering","slug":"an-interactive-multi-modal-query-answering","title":"An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Models","date":"2024-07-05","arxiv_id":"2407.04217","n_code_links":1,"syntology":null},{"paper":null,"slug":"hcs-tnas-hybrid-constraint-driven-semi","title":"HCS-TNAS: Hybrid Constraint-driven Semi-supervised Transformer-NAS for Ultrasound Image Segmentation","date":"2024-07-05","arxiv_id":"2407.04203","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffretouch-using-diffusion-to-retouch-on-the","title":"DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts","date":"2024-07-04","arxiv_id":"2407.03757","n_code_links":0,"syntology":null},{"paper":null,"slug":"medrat-unpaired-medical-report-generation-via","title":"MedRAT: Unpaired Medical Report Generation via Auxiliary Tasks","date":"2024-07-04","arxiv_id":"2407.03919","n_code_links":0,"syntology":null},{"paper":"/paper/a-unified-framework-for-3d-scene","slug":"a-unified-framework-for-3d-scene","title":"A Unified Framework for 3D Scene Understanding","date":"2024-07-03","arxiv_id":"2407.03263","n_code_links":1,"syntology":{"ran":7,"of":7,"n_ran_checked":7,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["dk-liang/uniseg3d"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"align-and-aggregate-compositional-reasoning-1","title":"Align and Aggregate: Compositional Reasoning with Video Alignment and Answer Aggregation for Video Question-Answering","date":"2024-07-03","arxiv_id":"2407.03008","n_code_links":0,"syntology":null},{"paper":"/paper/contrast-then-memorize-semantic-neighbor","slug":"contrast-then-memorize-semantic-neighbor","title":"Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph Completion","date":"2024-07-03","arxiv_id":"2407.02867","n_code_links":1,"syntology":null},{"paper":"/paper/non-adversarial-learning-vector-quantized","slug":"non-adversarial-learning-vector-quantized","title":"Non-Adversarial Learning: Vector-Quantized Common Latent Space for Multi-Sequence MRI","date":"2024-07-03","arxiv_id":"2407.02911","n_code_links":1,"syntology":null},{"paper":null,"slug":"supporting-cross-language-cross-project-bug","title":"Supporting Cross-language Cross-project Bug Localization Using Pre-trained Language Models","date":"2024-07-03","arxiv_id":"2407.02732","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-attention-based-contrastive-learning","title":"Towards Attention-based Contrastive Learning for Audio Spoof Detection","date":"2024-07-03","arxiv_id":"2407.03514","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-contrastive-learning-based-convolutional","title":"A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces","date":"2024-07-02","arxiv_id":"2407.04738","n_code_links":0,"syntology":null},{"paper":null,"slug":"drugclip-contrastive-drug-disease-interaction","title":"DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing","date":"2024-07-02","arxiv_id":"2407.02265","n_code_links":0,"syntology":null},{"paper":"/paper/hc-glad-dual-hyperbolic-contrastive-learning","slug":"hc-glad-dual-hyperbolic-contrastive-learning","title":"HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly Detection","date":"2024-07-02","arxiv_id":"2407.02057","n_code_links":1,"syntology":null},{"paper":"/paper/multi-grained-contrast-for-data-efficient","slug":"multi-grained-contrast-for-data-efficient","title":"Multi-Grained Contrast for Data-Efficient Unsupervised Representation Learning","date":"2024-07-02","arxiv_id":"2407.02014","n_code_links":1,"syntology":null},{"paper":"/paper/cgrclust-chaos-game-representation-for-twin","slug":"cgrclust-chaos-game-representation-for-twin","title":"CGRclust: Chaos Game Representation for Twin Contrastive Clustering of Unlabelled DNA Sequences","date":"2024-07-01","arxiv_id":"2407.02538","n_code_links":1,"syntology":null},{"paper":"/paper/robust-and-reliable-early-stage-website","slug":"robust-and-reliable-early-stage-website","title":"Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution Analysis","date":"2024-07-01","arxiv_id":"2407.00918","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-travel-decision-making-a","title":"Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations","date":"2024-06-30","arxiv_id":"2407.00787","n_code_links":0,"syntology":null},{"paper":null,"slug":"heterogeneous-graph-contrastive-learning-with-1","title":"Heterogeneous Graph Contrastive Learning with Spectral Augmentation","date":"2024-06-30","arxiv_id":"2407.00708","n_code_links":0,"syntology":null},{"paper":"/paper/safe-a-sar-feature-extractor-based-on-self","slug":"safe-a-sar-feature-extractor-based-on-self","title":"SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTs","date":"2024-06-30","arxiv_id":"2407.00851","n_code_links":1,"syntology":null},{"paper":null,"slug":"llms-as-instructors-learning-from-errors","title":"LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement","date":"2024-06-29","arxiv_id":"2407.00497","n_code_links":0,"syntology":null},{"paper":null,"slug":"emoe-tracker-environmental-moe-based","title":"eMoE-Tracker: Environmental MoE-based Transformer for Robust Event-guided Object Tracking","date":"2024-06-28","arxiv_id":"2406.20024","n_code_links":0,"syntology":null},{"paper":null,"slug":"infonce-identifying-the-gap-between-theory","title":"InfoNCE: Identifying the Gap Between Theory and Practice","date":"2024-06-28","arxiv_id":"2407.00143","n_code_links":0,"syntology":null},{"paper":"/paper/investigating-and-defending-shortcut-learning","slug":"investigating-and-defending-shortcut-learning","title":"Rethinking and Defending Protective Perturbation in Personalized Diffusion Models","date":"2024-06-27","arxiv_id":"2406.18944","n_code_links":1,"syntology":null},{"paper":null,"slug":"leveraging-contrastive-learning-for-enhanced","title":"Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers","date":"2024-06-27","arxiv_id":"2406.19258","n_code_links":0,"syntology":null},{"paper":null,"slug":"local-manifold-learning-for-no-reference","title":"Local Manifold Learning for No-Reference Image Quality Assessment","date":"2024-06-27","arxiv_id":"2406.19247","n_code_links":0,"syntology":null},{"paper":null,"slug":"protogmm-multi-prototype-gaussian-mixture","title":"ProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation","date":"2024-06-27","arxiv_id":"2406.19225","n_code_links":0,"syntology":null},{"paper":null,"slug":"zero-shot-domain-adaptation-based-on-dual","title":"Zero-shot domain adaptation based on dual-level mix and contrast","date":"2024-06-27","arxiv_id":"2406.18996","n_code_links":0,"syntology":null},{"paper":"/paper/denoising-as-adaptation-noise-space-domain","slug":"denoising-as-adaptation-noise-space-domain","title":"Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration","date":"2024-06-26","arxiv_id":"2406.18516","n_code_links":1,"syntology":{"ran":17,"of":20,"n_ran_checked":17,"n_instrument":0,"unverified":3,"pointer_only":20,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 1 honoured, 3 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["kangliao929/noise-da"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/improving-the-consistency-in-cross-lingual","slug":"improving-the-consistency-in-cross-lingual","title":"Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning","date":"2024-06-26","arxiv_id":"2406.18254","n_code_links":1,"syntology":null},{"paper":"/paper/selective-prompting-tuning-for-personalized","slug":"selective-prompting-tuning-for-personalized","title":"Selective Prompting Tuning for Personalized Conversations with LLMs","date":"2024-06-26","arxiv_id":"2406.18187","n_code_links":1,"syntology":{"ran":16,"of":16,"n_ran_checked":16,"n_instrument":0,"unverified":0,"pointer_only":16,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hqsiswiliam/SPT"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"data-curation-via-joint-example-selection","title":"Data curation via joint example selection further accelerates multimodal learning","date":"2024-06-25","arxiv_id":"2406.17711","n_code_links":0,"syntology":null},{"paper":null,"slug":"hyperbolic-knowledge-transfer-in-cross-domain","title":"Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System","date":"2024-06-25","arxiv_id":"2406.17289","n_code_links":0,"syntology":null},{"paper":"/paper/retrieval-style-in-context-learning-for-few","slug":"retrieval-style-in-context-learning-for-few","title":"Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification","date":"2024-06-25","arxiv_id":"2406.17534","n_code_links":1,"syntology":null},{"paper":"/paper/topogcl-topological-graph-contrastive","slug":"topogcl-topological-graph-contrastive","title":"TopoGCL: Topological Graph Contrastive Learning","date":"2024-06-25","arxiv_id":"2406.17251","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["topogclaaai24/topogcl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/video-inpainting-localization-with","slug":"video-inpainting-localization-with","title":"Video Inpainting Localization with Contrastive Learning","date":"2024-06-25","arxiv_id":"2406.17628","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-test-time-adaptation-for-object","title":"Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments","date":"2024-06-24","arxiv_id":"2406.16439","n_code_links":0,"syntology":null},{"paper":"/paper/learning-temporal-distances-contrastive","slug":"learning-temporal-distances-contrastive","title":"Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making","date":"2024-06-24","arxiv_id":"2406.17098","n_code_links":1,"syntology":{"ran":11,"of":11,"n_ran_checked":11,"n_instrument":0,"unverified":0,"pointer_only":11,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["vivekmyers/contrastive_metrics"],"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":["found_in_text","official"]}}},{"paper":"/paper/oaml-outlier-aware-metric-learning-for-ood","slug":"oaml-outlier-aware-metric-learning-for-ood","title":"Enhancing OOD Detection Using Latent Diffusion","date":"2024-06-24","arxiv_id":"2406.16525","n_code_links":1,"syntology":null},{"paper":"/paper/musecl-predicting-urban-socioeconomic","slug":"musecl-predicting-urban-socioeconomic","title":"MuseCL: Predicting Urban Socioeconomic Indicators via Multi-Semantic Contrastive Learning","date":"2024-06-23","arxiv_id":"2407.09523","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["xixianyong/musecl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/fine-grained-background-representation-for","slug":"fine-grained-background-representation-for","title":"Fine-grained Background Representation for Weakly Supervised Semantic Segmentation","date":"2024-06-22","arxiv_id":"2406.15755","n_code_links":1,"syntology":null},{"paper":null,"slug":"self-supervised-alignment-learning-for","title":"Self-Supervised Alignment Learning for Medical Image Segmentation","date":"2024-06-22","arxiv_id":"2406.15699","n_code_links":0,"syntology":null},{"paper":"/paper/speech-analysis-of-language-varieties-in","slug":"speech-analysis-of-language-varieties-in","title":"Speech Analysis of Language Varieties in Italy","date":"2024-06-22","arxiv_id":"2406.15862","n_code_links":1,"syntology":null},{"paper":null,"slug":"dn-cl-deep-symbolic-regression-against-noise","title":"DN-CL: Deep Symbolic Regression against Noise via Contrastive Learning","date":"2024-06-21","arxiv_id":"2406.14844","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-idiomatic-representation-in","title":"Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss","date":"2024-06-21","arxiv_id":"2406.15175","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-overfitting-to-robustness-quantity","title":"From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning","date":"2024-06-21","arxiv_id":"2406.15044","n_code_links":0,"syntology":null},{"paper":null,"slug":"temprompt-multi-task-prompt-learning-for","title":"TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems","date":"2024-06-21","arxiv_id":"2406.14825","n_code_links":0,"syntology":null},{"paper":"/paper/a-contrastive-learning-approach-to-mitigate","slug":"a-contrastive-learning-approach-to-mitigate","title":"A Contrastive Learning Approach to Mitigate Bias in Speech Models","date":"2024-06-20","arxiv_id":"2406.14686","n_code_links":1,"syntology":null},{"paper":null,"slug":"factual-dialogue-summarization-via-learning","title":"Factual Dialogue Summarization via Learning from Large Language Models","date":"2024-06-20","arxiv_id":"2406.14709","n_code_links":0,"syntology":null},{"paper":"/paper/larp-language-audio-relational-pre-training","slug":"larp-language-audio-relational-pre-training","title":"LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation","date":"2024-06-20","arxiv_id":"2406.14333","n_code_links":1,"syntology":null},{"paper":null,"slug":"maintenance-required-updating-and-extending","title":"Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes","date":"2024-06-20","arxiv_id":"2406.14446","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-modularity-maximization-for-graph","slug":"revisiting-modularity-maximization-for-graph","title":"Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective","date":"2024-06-20","arxiv_id":"2406.14288","n_code_links":1,"syntology":null},{"paper":"/paper/unifying-graph-convolution-and-contrastive","slug":"unifying-graph-convolution-and-contrastive","title":"Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering","date":"2024-06-20","arxiv_id":"2406.13996","n_code_links":1,"syntology":null},{"paper":null,"slug":"composite-concept-extraction-through","title":"Composite Concept Extraction through Backdooring","date":"2024-06-19","arxiv_id":"2406.13411","n_code_links":0,"syntology":null},{"paper":"/paper/towards-a-multimodal-framework-for-remote","slug":"towards-a-multimodal-framework-for-remote","title":"Towards a multimodal framework for remote sensing image change retrieval and captioning","date":"2024-06-19","arxiv_id":"2406.13424","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-generic-method-for-fine-grained-category","title":"A Generic Method for Fine-grained Category Discovery in Natural Language Texts","date":"2024-06-18","arxiv_id":"2406.13103","n_code_links":0,"syntology":null},{"paper":"/paper/bioscan-5m-a-multimodal-dataset-for-insect","slug":"bioscan-5m-a-multimodal-dataset-for-insect","title":"BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity","date":"2024-06-18","arxiv_id":"2406.12723","n_code_links":2,"syntology":{"ran":5,"of":8,"n_ran_checked":4,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["bioscan-ml/dataset"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["named_in_paper","official"]}}},{"paper":"/paper/effective-generation-of-feasible-solutions","slug":"effective-generation-of-feasible-solutions","title":"Effective Generation of Feasible Solutions for Integer Programming via Guided Diffusion","date":"2024-06-18","arxiv_id":"2406.12349","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":6,"n_instrument":0,"unverified":0,"pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["agent-lab/diffusion-integer-programming"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/groprompt-efficient-grounded-prompting-and","slug":"groprompt-efficient-grounded-prompting-and","title":"GroPrompt: Efficient Grounded Prompting and Adaptation for Referring Video Object Segmentation","date":"2024-06-18","arxiv_id":"2406.12834","n_code_links":0,"syntology":null},{"paper":"/paper/rigl-a-unified-reciprocal-approach-for","slug":"rigl-a-unified-reciprocal-approach-for","title":"RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes","date":"2024-06-18","arxiv_id":"2406.12465","n_code_links":1,"syntology":{"ran":5,"of":8,"n_ran_checked":5,"n_instrument":0,"unverified":3,"pointer_only":8,"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) · 3 unverified","official":{"repos":["labyrinthineleo/rigl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"spatially-resolved-gene-expression-prediction-2","title":"Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization","date":"2024-06-18","arxiv_id":"2406.12229","n_code_links":0,"syntology":null},{"paper":null,"slug":"toward-exploring-the-code-understanding","title":"Toward Exploring the Code Understanding Capabilities of Pre-trained Code Generation Models","date":"2024-06-18","arxiv_id":"2406.12326","n_code_links":0,"syntology":null},{"paper":"/paper/visually-robust-adversarial-imitation","slug":"visually-robust-adversarial-imitation","title":"Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning","date":"2024-06-18","arxiv_id":"2407.12792","n_code_links":1,"syntology":null},{"paper":"/paper/balancing-embedding-spectrum-for","slug":"balancing-embedding-spectrum-for","title":"Balancing Embedding Spectrum for Recommendation","date":"2024-06-17","arxiv_id":"2406.12032","n_code_links":1,"syntology":null},{"paper":"/paper/can-machines-resonate-with-humans-evaluating","slug":"can-machines-resonate-with-humans-evaluating","title":"Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs","date":"2024-06-17","arxiv_id":"2406.11250","n_code_links":1,"syntology":null},{"paper":"/paper/diffmm-multi-modal-diffusion-model-for","slug":"diffmm-multi-modal-diffusion-model-for","title":"DiffMM: Multi-Modal Diffusion Model for Recommendation","date":"2024-06-17","arxiv_id":"2406.11781","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-generalizability-of-representation","title":"Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding","date":"2024-06-17","arxiv_id":"2406.11283","n_code_links":0,"syntology":null},{"paper":null,"slug":"internalinspector-i-2-robust-confidence","title":"InternalInspector $I^2$: Robust Confidence Estimation in LLMs through Internal States","date":"2024-06-17","arxiv_id":"2406.12053","n_code_links":0,"syntology":null},{"paper":"/paper/minicongts-a-near-ultimate-minimalist","slug":"minicongts-a-near-ultimate-minimalist","title":"MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction","date":"2024-06-17","arxiv_id":"2406.11234","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["qiaosun22/MiniConGTS"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/mix-domain-contrastive-learning-for-unpaired","slug":"mix-domain-contrastive-learning-for-unpaired","title":"Mix-Domain Contrastive Learning for Unpaired H&E-to-IHC Stain Translation","date":"2024-06-17","arxiv_id":"2406.11799","n_code_links":1,"syntology":{"ran":3,"of":7,"n_ran_checked":1,"n_instrument":2,"unverified":4,"pointer_only":7,"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) · 4 unverified","official":{"repos":["ssongwang/mix-domaincontrastivelearning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/on-the-effectiveness-of-supervision-in","slug":"on-the-effectiveness-of-supervision-in","title":"On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning","date":"2024-06-16","arxiv_id":"2406.10815","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":1,"n_instrument":5,"unverified":2,"pointer_only":0,"phrase":"6 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; 5 where Syntology's instrument failed) · 2 unverified","official":{"repos":["jh-oh-23/sup-ancl"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/self-supervised-representation-learning-with-5","slug":"self-supervised-representation-learning-with-5","title":"Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language Recognition","date":"2024-06-15","arxiv_id":"2406.10501","n_code_links":1,"syntology":null},{"paper":"/paper/semanticmim-marring-masked-image-modeling","slug":"semanticmim-marring-masked-image-modeling","title":"SemanticMIM: Marring Masked Image Modeling with Semantics Compression for General Visual Representation","date":"2024-06-15","arxiv_id":"2406.10673","n_code_links":1,"syntology":null}],"record_sha256":"1cb54d0c4034fa6ebdae501c58b4d3aea648212becb419a7baa702d7ef72b3c7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}