{"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/representation-learning/papers/14","list_of":"/task/representation-learning","task":"Representation 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":14,"pages_in_order":106,"rows_per_page":100,"rows":[1301,1400],"of":10580,"counts":{"archive_papers_tagged":10580,"with_a_code_link":4662,"where_syntology_ran_a_sample":1439,"not_listed_spam_title":0,"listed":10580,"listed_where_code_ran":1439,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1228,"every_run_a_failure_of_syntologys_instrument":211,"listed_with_a_run_with_no_instrument_failure":1228,"listed_every_run_a_failure_of_syntologys_instrument":211,"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/representation-learning","prev":"/task/representation-learning/papers/13","next":"/task/representation-learning/papers/15","papers":[{"url":"/paper/balanced-multi-relational-graph-clustering","slug":"balanced-multi-relational-graph-clustering","title":"Balanced Multi-Relational Graph Clustering","date":"2024-07-23","arxiv_id":"2407.16863","repositories_listed":1,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":10,"n_instrument":5,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 2 honoured, 0 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/balanced-multi-relational-graph-clustering#ran","syntology_url":"https://syntology.ai/paper/2407.16863","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.16863"}},"official":{"repos":["zxlearningdeep/bmgc"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/coarse-to-fine-proposal-refinement-framework","slug":"coarse-to-fine-proposal-refinement-framework","title":"Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization","date":"2024-07-23","arxiv_id":"2407.16554","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-temporal-cross-view-contrastive-pre","slug":"spatial-temporal-cross-view-contrastive-pre","title":"Spatial-Temporal Cross-View Contrastive Pre-training for Check-in Sequence Representation Learning","date":"2024-07-22","arxiv_id":"2407.15899","repositories_listed":1,"syntology":null},{"url":"/paper/towards-latent-masked-image-modeling-for-self","slug":"towards-latent-masked-image-modeling-for-self","title":"Towards Latent Masked Image Modeling for Self-Supervised Visual Representation Learning","date":"2024-07-22","arxiv_id":"2407.15837","repositories_listed":1,"syntology":{"n":29,"n_ran":21,"n_constructed":10,"n_ran_checked":19,"n_instrument":2,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":29,"phrase":"21 ran (of which 10 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 2 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/towards-latent-masked-image-modeling-for-self#ran","syntology_url":"https://syntology.ai/paper/2407.15837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.15837"}},"official":{"repos":["yibingwei-1/latentmim"],"state":"official (archive's flag): 21 ran","n_ran":21,"n_constructed":10,"n_ran_no_instrument_failure":19,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/crossdehaze-scaling-up-image-dehazing-with","slug":"crossdehaze-scaling-up-image-dehazing-with","title":"Scaling Up Single Image Dehazing Algorithm by Cross-Data Vision Alignment for Richer Representation Learning and Beyond","date":"2024-07-20","arxiv_id":"2407.14823","repositories_listed":1,"syntology":null},{"url":"/paper/disensemi-semi-supervised-graph","slug":"disensemi-semi-supervised-graph","title":"DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning","date":"2024-07-19","arxiv_id":"2407.14081","repositories_listed":1,"syntology":null},{"url":"/paper/polyformer-scalable-node-wise-filters-via","slug":"polyformer-scalable-node-wise-filters-via","title":"PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer","date":"2024-07-19","arxiv_id":"2407.14459","repositories_listed":1,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":14,"phrase":"10 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; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/polyformer-scalable-node-wise-filters-via#ran","syntology_url":"https://syntology.ai/paper/2407.14459","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.14459"}},"official":{"repos":["air029/polyformer"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-goal-conditioned-representations-for","slug":"learning-goal-conditioned-representations-for","title":"Learning Goal-Conditioned Representations for Language Reward Models","date":"2024-07-18","arxiv_id":"2407.13887","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-aware-representation-learning-for","slug":"semantic-aware-representation-learning-for","title":"Semantic-aware Representation Learning for Homography Estimation","date":"2024-07-18","arxiv_id":"2407.13284","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-diffusions-in-latent-space-for","slug":"denoising-diffusions-in-latent-space-for","title":"Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracy","date":"2024-07-17","arxiv_id":"2407.12952","repositories_listed":1,"syntology":null},{"url":"/paper/an-ai-system-for-continuous-knee","slug":"an-ai-system-for-continuous-knee","title":"An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data","date":"2024-07-16","arxiv_id":"2407.11500","repositories_listed":1,"syntology":null},{"url":"/paper/distractors-immune-representation-learning","slug":"distractors-immune-representation-learning","title":"Distractors-Immune Representation Learning with Cross-modal Contrastive Regularization for Change Captioning","date":"2024-07-16","arxiv_id":"2407.11683","repositories_listed":1,"syntology":null},{"url":"/paper/isometric-representation-learning-for","slug":"isometric-representation-learning-for","title":"Isometric Representation Learning for Disentangled Latent Space of Diffusion Models","date":"2024-07-16","arxiv_id":"2407.11451","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":16,"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) · 5 unverified","sample_list":"/paper/isometric-representation-learning-for#ran","syntology_url":"https://syntology.ai/paper/2407.11451","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.11451"}},"official":{"repos":["isno0907/isodiff"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-unsupervised-visual-representation","slug":"efficient-unsupervised-visual-representation","title":"Efficient Unsupervised Visual Representation Learning with Explicit Cluster Balancing","date":"2024-07-15","arxiv_id":"2407.11168","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-and-identity","slug":"representation-learning-and-identity","title":"Representation Learning and Identity Adversarial Training for Facial Behavior Understanding","date":"2024-07-15","arxiv_id":"2407.11243","repositories_listed":1,"syntology":null},{"url":"/paper/shape2scene-3d-scene-representation-learning","slug":"shape2scene-3d-scene-representation-learning","title":"Shape2Scene: 3D Scene Representation Learning Through Pre-training on Shape Data","date":"2024-07-14","arxiv_id":"2407.10200","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-role-of-discrete-tokenization-in","slug":"on-the-role-of-discrete-tokenization-in","title":"On the Role of Discrete Tokenization in Visual Representation Learning","date":"2024-07-12","arxiv_id":"2407.09087","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":1,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-the-role-of-discrete-tokenization-in#ran","syntology_url":"https://syntology.ai/paper/2407.09087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.09087"}},"official":{"repos":["pku-ml/clustermim"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/one-stone-four-birds-a-comprehensive-solution","slug":"one-stone-four-birds-a-comprehensive-solution","title":"One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning","date":"2024-07-12","arxiv_id":"2407.09011","repositories_listed":1,"syntology":null},{"url":"/paper/slidegcd-slide-based-graph-collaborative","slug":"slidegcd-slide-based-graph-collaborative","title":"SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification","date":"2024-07-12","arxiv_id":"2407.08968","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-graph-representation-learning","slug":"unsupervised-graph-representation-learning","title":"Unsupervised Graph Representation Learning with Inductive Shallow Node Embedding","date":"2024-07-12","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/emergent-visual-semantic-hierarchies-in-image","slug":"emergent-visual-semantic-hierarchies-in-image","title":"Emergent Visual-Semantic Hierarchies in Image-Text Representations","date":"2024-07-11","arxiv_id":"2407.08521","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/emergent-visual-semantic-hierarchies-in-image#ran","syntology_url":"https://syntology.ai/paper/2407.08521","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.08521"}},"official":{"repos":["TAU-VAILab/hierarcaps"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exemplar-free-continual-representation","slug":"exemplar-free-continual-representation","title":"Exemplar-free Continual Representation Learning via Learnable Drift Compensation","date":"2024-07-11","arxiv_id":"2407.08536","repositories_listed":1,"syntology":{"n":16,"n_ran":8,"n_constructed":6,"n_ran_checked":8,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":16,"phrase":"8 ran (of which 6 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/exemplar-free-continual-representation#ran","syntology_url":"https://syntology.ai/paper/2407.08536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.08536"}},"official":{"repos":["alviur/ldc"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":6,"n_ran_no_instrument_failure":8,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/projecting-points-to-axes-oriented-object","slug":"projecting-points-to-axes-oriented-object","title":"Projecting Points to Axes: Oriented Object Detection via Point-Axis Representation","date":"2024-07-11","arxiv_id":"2407.08489","repositories_listed":1,"syntology":null},{"url":"/paper/a-coding-theoretic-analysis-of-hyperspherical","slug":"a-coding-theoretic-analysis-of-hyperspherical","title":"A Coding-Theoretic Analysis of Hyperspherical Prototypical Learning Geometry","date":"2024-07-10","arxiv_id":"2407.07664","repositories_listed":1,"syntology":null},{"url":"/paper/avcap-leveraging-audio-visual-features-as","slug":"avcap-leveraging-audio-visual-features-as","title":"AVCap: Leveraging Audio-Visual Features as Text Tokens for Captioning","date":"2024-07-10","arxiv_id":"2407.07801","repositories_listed":1,"syntology":null},{"url":"/paper/pan-cancer-histopathology-wsi-pre-training","slug":"pan-cancer-histopathology-wsi-pre-training","title":"Pan-cancer Histopathology WSI Pre-training with Position-aware Masked Autoencoder","date":"2024-07-10","arxiv_id":"2407.07504","repositories_listed":1,"syntology":null},{"url":"/paper/posformer-recognizing-complex-handwritten","slug":"posformer-recognizing-complex-handwritten","title":"PosFormer: Recognizing Complex Handwritten Mathematical Expression with Position Forest Transformer","date":"2024-07-10","arxiv_id":"2407.07764","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/posformer-recognizing-complex-handwritten#ran","syntology_url":"https://syntology.ai/paper/2407.07764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07764"}},"official":{"repos":["sjtu-deepvisionlab/posformer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/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","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":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","sample_list":"/paper/tip-tabular-image-pre-training-for-multimodal#ran","syntology_url":"https://syntology.ai/paper/2407.07582","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07582"}},"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"]}}},{"url":"/paper/ctrl-f-pairing-convolution-with-transformer","slug":"ctrl-f-pairing-convolution-with-transformer","title":"CTRL-F: Pairing Convolution with Transformer for Image Classification via Multi-Level Feature Cross-Attention and Representation Learning Fusion","date":"2024-07-09","arxiv_id":"2407.06673","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-visual-learning-from","slug":"self-supervised-visual-learning-from","title":"Self-supervised visual learning from interactions with objects","date":"2024-07-09","arxiv_id":"2407.06704","repositories_listed":1,"syntology":null},{"url":"/paper/te-ssl-time-and-event-aware-self-supervised","slug":"te-ssl-time-and-event-aware-self-supervised","title":"TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression Analysis","date":"2024-07-09","arxiv_id":"2407.06852","repositories_listed":1,"syntology":null},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/hide-pet-continual-learning-via-hierarchical","slug":"hide-pet-continual-learning-via-hierarchical","title":"HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient Tuning","date":"2024-07-07","arxiv_id":"2407.05229","repositories_listed":1,"syntology":null},{"url":"/paper/ptarl-prototype-based-tabular-representation-1","slug":"ptarl-prototype-based-tabular-representation-1","title":"PTaRL: Prototype-based Tabular Representation Learning via Space Calibration","date":"2024-07-07","arxiv_id":"2407.05364","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":3,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ptarl-prototype-based-tabular-representation-1#ran","syntology_url":"https://syntology.ai/paper/2407.05364","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05364"}},"official":null}},{"url":"/paper/adversarial-robustness-of-vaes-across","slug":"adversarial-robustness-of-vaes-across","title":"Adversarial Robustness of VAEs across Intersectional Subgroups","date":"2024-07-04","arxiv_id":"2407.03864","repositories_listed":1,"syntology":null},{"url":"/paper/do-generalised-classifiers-really-work-on","slug":"do-generalised-classifiers-really-work-on","title":"Do Generalised Classifiers really work on Human Drawn Sketches?","date":"2024-07-04","arxiv_id":"2407.03893","repositories_listed":1,"syntology":null},{"url":"/paper/meta-optimized-angular-margin-contrastive","slug":"meta-optimized-angular-margin-contrastive","title":"MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning","date":"2024-07-04","arxiv_id":"2407.03788","repositories_listed":1,"syntology":null},{"url":"/paper/flowcon-out-of-distribution-detection-using","slug":"flowcon-out-of-distribution-detection-using","title":"FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive Learning","date":"2024-07-03","arxiv_id":"2407.03489","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":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/flowcon-out-of-distribution-detection-using#ran","syntology_url":"https://syntology.ai/paper/2407.03489","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.03489"}},"official":{"repos":["saandeepa93/FlowCon_OOD"],"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/stable-heterogeneous-treatment-effect","slug":"stable-heterogeneous-treatment-effect","title":"Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations","date":"2024-07-03","arxiv_id":"2407.03082","repositories_listed":1,"syntology":null},{"url":"/paper/extracting-and-encoding-leveraging-large","slug":"extracting-and-encoding-leveraging-large","title":"Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation","date":"2024-07-02","arxiv_id":"2407.01948","repositories_listed":1,"syntology":null},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/semantically-guided-representation-learning","slug":"semantically-guided-representation-learning","title":"Semantically Guided Representation Learning For Action Anticipation","date":"2024-07-02","arxiv_id":"2407.02309","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":4,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/semantically-guided-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2407.02309","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.02309"}},"official":{"repos":["ADiko1997/S-GEAR"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/siamtst-a-novel-representation-learning","slug":"siamtst-a-novel-representation-learning","title":"SiamTST: A Novel Representation Learning Framework for Enhanced Multivariate Time Series Forecasting applied to Telco Networks","date":"2024-07-02","arxiv_id":"2407.02258","repositories_listed":1,"syntology":null},{"url":"/paper/uniform-transformation-refining-latent","slug":"uniform-transformation-refining-latent","title":"Uniform Transformation: Refining Latent Representation in Variational Autoencoders","date":"2024-07-02","arxiv_id":"2407.02681","repositories_listed":1,"syntology":null},{"url":"/paper/zeroddi-a-zero-shot-drug-drug-interaction","slug":"zeroddi-a-zero-shot-drug-drug-interaction","title":"ZeroDDI: A Zero-Shot Drug-Drug Interaction Event Prediction Method with Semantic Enhanced Learning and Dual-Modal Uniform Alignment","date":"2024-07-01","arxiv_id":"2407.00891","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/zeroddi-a-zero-shot-drug-drug-interaction#ran","syntology_url":"https://syntology.ai/paper/2407.00891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.00891"}},"official":{"repos":["wzy-sarah/zeroddi"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-models-and-representation-learning","slug":"diffusion-models-and-representation-learning","title":"Diffusion Models and Representation Learning: A Survey","date":"2024-06-30","arxiv_id":"2407.00783","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-personalized-text-to-image","slug":"efficient-personalized-text-to-image","title":"Efficient Personalized Text-to-image Generation by Leveraging Textual Subspace","date":"2024-06-30","arxiv_id":"2407.00608","repositories_listed":1,"syntology":null},{"url":"/paper/establishing-deep-infomax-as-an-effective","slug":"establishing-deep-infomax-as-an-effective","title":"Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics","date":"2024-06-30","arxiv_id":"2407.00671","repositories_listed":1,"syntology":null},{"url":"/paper/polygongnn-representation-learning-for","slug":"polygongnn-representation-learning-for","title":"PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility Graph","date":"2024-06-30","arxiv_id":"2407.00742","repositories_listed":1,"syntology":null},{"url":"/paper/tabsketchfm-sketch-based-tabular","slug":"tabsketchfm-sketch-based-tabular","title":"TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes","date":"2024-06-28","arxiv_id":"2407.01619","repositories_listed":1,"syntology":null},{"url":"/paper/fibottention-inceptive-visual-representation","slug":"fibottention-inceptive-visual-representation","title":"Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across Heads","date":"2024-06-27","arxiv_id":"2406.19391","repositories_listed":1,"syntology":null},{"url":"/paper/heterogeneous-causal-metapath-graph-neural","slug":"heterogeneous-causal-metapath-graph-neural","title":"Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction","date":"2024-06-27","arxiv_id":"2406.19156","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/heterogeneous-causal-metapath-graph-neural#ran","syntology_url":"https://syntology.ai/paper/2406.19156","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.19156"}},"official":{"repos":["zkxinxin/hcmgnn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/unirec-a-dual-enhancement-of-uniformity-and","slug":"unirec-a-dual-enhancement-of-uniformity-and","title":"UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential Recommendations","date":"2024-06-26","arxiv_id":"2406.18470","repositories_listed":1,"syntology":null},{"url":"/paper/mpcoder-multi-user-personalized-code","slug":"mpcoder-multi-user-personalized-code","title":"MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning","date":"2024-06-25","arxiv_id":"2406.17255","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"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) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/mpcoder-multi-user-personalized-code#ran","syntology_url":"https://syntology.ai/paper/2406.17255","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17255"}},"official":{"repos":["455849940/MPCoder"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/se-vgae-unsupervised-disentangled","slug":"se-vgae-unsupervised-disentangled","title":"SE-VGAE: Unsupervised Disentangled Representation Learning for Interpretable Architectural Layout Design Graph Generation","date":"2024-06-25","arxiv_id":"2406.17418","repositories_listed":1,"syntology":null},{"url":"/paper/when-does-self-prediction-help-understanding","slug":"when-does-self-prediction-help-understanding","title":"When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning","date":"2024-06-25","arxiv_id":"2406.17718","repositories_listed":1,"syntology":null},{"url":"/paper/cambrian-1-a-fully-open-vision-centric","slug":"cambrian-1-a-fully-open-vision-centric","title":"Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs","date":"2024-06-24","arxiv_id":"2406.16860","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":3,"n_ran_checked":5,"n_instrument":5,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"10 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cambrian-1-a-fully-open-vision-centric#ran","syntology_url":"https://syntology.ai/paper/2406.16860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.16860"}},"official":{"repos":["cambrian-mllm/cambrian"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/geomformer-a-general-architecture-for","slug":"geomformer-a-general-architecture-for","title":"GeoMFormer: A General Architecture for Geometric Molecular Representation Learning","date":"2024-06-24","arxiv_id":"2406.16853","repositories_listed":1,"syntology":null},{"url":"/paper/unipsda-unsupervised-pseudo-semantic-data","slug":"unipsda-unsupervised-pseudo-semantic-data","title":"UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding","date":"2024-06-24","arxiv_id":"2406.16372","repositories_listed":1,"syntology":null},{"url":"/paper/hest-1k-a-dataset-for-spatial-transcriptomics","slug":"hest-1k-a-dataset-for-spatial-transcriptomics","title":"HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis","date":"2024-06-23","arxiv_id":"2406.16192","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hest-1k-a-dataset-for-spatial-transcriptomics#ran","syntology_url":"https://syntology.ai/paper/2406.16192","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.16192"}},"official":{"repos":["mahmoodlab/hest"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/clip-decoder-zeroshot-multilabel","slug":"clip-decoder-zeroshot-multilabel","title":"CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation","date":"2024-06-21","arxiv_id":"2406.14830","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/clip-decoder-zeroshot-multilabel#ran","syntology_url":"https://syntology.ai/paper/2406.14830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14830"}},"official":null}},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/mm-gtunets-unified-multi-modal-graph-deep","slug":"mm-gtunets-unified-multi-modal-graph-deep","title":"MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction","date":"2024-06-20","arxiv_id":"2406.14455","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-of-spatially-embedded-networks-via","slug":"modeling-of-spatially-embedded-networks-via","title":"Modeling of spatially embedded networks via regional spatial graph convolutional networks","date":"2024-06-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/endouic-promptable-diffusion-transformer-for","slug":"endouic-promptable-diffusion-transformer-for","title":"EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy","date":"2024-06-19","arxiv_id":"2406.13705","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-representation-learning-on-the","slug":"evaluating-representation-learning-on-the","title":"Evaluating representation learning on the protein structure universe","date":"2024-06-19","arxiv_id":"2406.13864","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":13,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/evaluating-representation-learning-on-the#ran","syntology_url":"https://syntology.ai/paper/2406.13864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13864"}},"official":{"repos":["a-r-j/proteinworkshop"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/bridging-local-details-and-global-context-in","slug":"bridging-local-details-and-global-context-in","title":"Bridging Local Details and Global Context in Text-Attributed Graphs","date":"2024-06-18","arxiv_id":"2406.12608","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/bridging-local-details-and-global-context-in#ran","syntology_url":"https://syntology.ai/paper/2406.12608","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12608"}},"official":{"repos":["wykk00/graphbridge"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/virl-volume-informed-representation-learning","slug":"virl-volume-informed-representation-learning","title":"VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimation","date":"2024-06-18","arxiv_id":"2406.12286","repositories_listed":1,"syntology":null},{"url":"/paper/an-interpretable-alternative-to-neural","slug":"an-interpretable-alternative-to-neural","title":"An Interpretable Alternative to Neural Representation Learning for Rating Prediction -- Transparent Latent Class Modeling of User Reviews","date":"2024-06-17","arxiv_id":"2407.00063","repositories_listed":1,"syntology":null},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/duoduo-clip-efficient-3d-understanding-with","slug":"duoduo-clip-efficient-3d-understanding-with","title":"Duoduo CLIP: Efficient 3D Understanding with Multi-View Images","date":"2024-06-17","arxiv_id":"2406.11579","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/duoduo-clip-efficient-3d-understanding-with#ran","syntology_url":"https://syntology.ai/paper/2406.11579","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11579"}},"official":{"repos":["3dlg-hcvc/DuoduoCLIP"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/learning-molecular-representation-in-a-cell","slug":"learning-molecular-representation-in-a-cell","title":"Learning Molecular Representation in a Cell","date":"2024-06-17","arxiv_id":"2406.12056","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-molecular-representation-in-a-cell#ran","syntology_url":"https://syntology.ai/paper/2406.12056","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12056"}},"official":{"repos":["liugangcode/InfoAlign"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/uniglm-training-one-unified-language-model","slug":"uniglm-training-one-unified-language-model","title":"UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding","date":"2024-06-17","arxiv_id":"2406.12052","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/uniglm-training-one-unified-language-model#ran","syntology_url":"https://syntology.ai/paper/2406.12052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12052"}},"official":{"repos":["nyushcs/uniglm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/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","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":1,"n_ran_checked":1,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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","sample_list":"/paper/on-the-effectiveness-of-supervision-in#ran","syntology_url":"https://syntology.ai/paper/2406.10815","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.10815"}},"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"]}}},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/learning-multi-view-molecular-representations","slug":"learning-multi-view-molecular-representations","title":"Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge","date":"2024-06-14","arxiv_id":"2406.09841","repositories_listed":1,"syntology":null},{"url":"/paper/towards-scalable-and-versatile-weight-space","slug":"towards-scalable-and-versatile-weight-space","title":"Towards Scalable and Versatile Weight Space Learning","date":"2024-06-14","arxiv_id":"2406.09997","repositories_listed":1,"syntology":{"n":15,"n_ran":11,"n_constructed":7,"n_ran_checked":11,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":15,"phrase":"11 ran (of which 7 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) · 4 unverified","sample_list":"/paper/towards-scalable-and-versatile-weight-space#ran","syntology_url":"https://syntology.ai/paper/2406.09997","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.09997"}},"official":{"repos":["hsg-aiml/sane"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":7,"n_ran_no_instrument_failure":11,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/denoisereid-denoising-model-for","slug":"denoisereid-denoising-model-for","title":"DenoiseRep: Denoising Model for Representation Learning","date":"2024-06-13","arxiv_id":"2406.08773","repositories_listed":1,"syntology":{"n":22,"n_ran":17,"n_constructed":2,"n_ran_checked":17,"n_instrument":0,"n_unverified":5,"n_honours":2,"n_violates":1,"n_no_contract":14,"n_pointer_only":6,"phrase":"17 ran (of which 2 constructed an object rather than computing a result; 17 with no instrument failure: 2 honoured, 1 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/denoisereid-denoising-model-for#ran","syntology_url":"https://syntology.ai/paper/2406.08773","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.08773"}},"official":{"repos":["wangguanan/denoiserep"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":2,"n_ran_no_instrument_failure":17,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/dsl-fiqa-assessing-facial-image-quality-via-1","slug":"dsl-fiqa-assessing-facial-image-quality-via-1","title":"DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer","date":"2024-06-13","arxiv_id":"2406.09622","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-anomaly-detection-via-category","slug":"few-shot-anomaly-detection-via-category","title":"Few-Shot Anomaly Detection via Category-Agnostic Registration Learning","date":"2024-06-13","arxiv_id":"2406.08810","repositories_listed":1,"syntology":null},{"url":"/paper/moleculecla-rethinking-molecular-benchmark","slug":"moleculecla-rethinking-molecular-benchmark","title":"MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis","date":"2024-06-13","arxiv_id":"2406.17797","repositories_listed":1,"syntology":null},{"url":"/paper/multiple-prior-representation-learning-for","slug":"multiple-prior-representation-learning-for","title":"Multiple Prior Representation Learning for Self-Supervised Monocular Depth Estimation via Hybrid Transformer","date":"2024-06-13","arxiv_id":"2406.08928","repositories_listed":1,"syntology":null},{"url":"/paper/olga-one-class-graph-autoencoder","slug":"olga-one-class-graph-autoencoder","title":"OLGA: One-cLass Graph Autoencoder","date":"2024-06-13","arxiv_id":"2406.09131","repositories_listed":1,"syntology":null},{"url":"/paper/accurate-explanation-model-for-image","slug":"accurate-explanation-model-for-image","title":"Accurate Explanation Model for Image Classifiers using Class Association Embedding","date":"2024-06-12","arxiv_id":"2406.07961","repositories_listed":1,"syntology":null},{"url":"/paper/balancing-molecular-information-and-empirical","slug":"balancing-molecular-information-and-empirical","title":"Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties","date":"2024-06-12","arxiv_id":"2406.08075","repositories_listed":1,"syntology":null},{"url":"/paper/dehazedct-towards-effective-non-homogeneous","slug":"dehazedct-towards-effective-non-homogeneous","title":"DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer","date":"2024-06-12","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficient-neural-common-neighbor-for-temporal","slug":"efficient-neural-common-neighbor-for-temporal","title":"Efficient Neural Common Neighbor for Temporal Graph Link Prediction","date":"2024-06-12","arxiv_id":"2406.07926","repositories_listed":1,"syntology":null},{"url":"/paper/mail-improving-imitation-learning-with-mamba","slug":"mail-improving-imitation-learning-with-mamba","title":"MaIL: Improving Imitation Learning with Mamba","date":"2024-06-12","arxiv_id":"2406.08234","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mail-improving-imitation-learning-with-mamba#ran","syntology_url":"https://syntology.ai/paper/2406.08234","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.08234"}},"official":{"repos":["alrhub/mail"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/tell-me-what-s-next-textual-foresight-for","slug":"tell-me-what-s-next-textual-foresight-for","title":"Tell Me What's Next: Textual Foresight for Generic UI Representations","date":"2024-06-12","arxiv_id":"2406.07822","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-vision-language-contrastive","slug":"benchmarking-vision-language-contrastive","title":"Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning","date":"2024-06-11","arxiv_id":"2406.07450","repositories_listed":1,"syntology":null},{"url":"/paper/discrete-dictionary-based-decomposition-layer","slug":"discrete-dictionary-based-decomposition-layer","title":"Discrete Dictionary-based Decomposition Layer for Structured Representation Learning","date":"2024-06-11","arxiv_id":"2406.06976","repositories_listed":1,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/discrete-dictionary-based-decomposition-layer#ran","syntology_url":"https://syntology.ai/paper/2406.06976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.06976"}},"official":{"repos":["taewonpark/d3"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/explaining-representation-learning-with","slug":"explaining-representation-learning-with","title":"Explaining Representation Learning with Perceptual Components","date":"2024-06-11","arxiv_id":"2406.06930","repositories_listed":1,"syntology":null},{"url":"/paper/identifiable-object-centric-representation","slug":"identifiable-object-centric-representation","title":"Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention","date":"2024-06-11","arxiv_id":"2406.07141","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/identifiable-object-centric-representation#ran","syntology_url":"https://syntology.ai/paper/2406.07141","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.07141"}},"official":{"repos":["koriavinash1/psa"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/improving-multi-hop-logical-reasoning-in","slug":"improving-multi-hop-logical-reasoning-in","title":"Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning","date":"2024-06-11","arxiv_id":"2406.07034","repositories_listed":1,"syntology":null},{"url":"/paper/matryoshka-representation-learning-for","slug":"matryoshka-representation-learning-for","title":"Matryoshka Representation Learning for Recommendation","date":"2024-06-11","arxiv_id":"2406.07432","repositories_listed":1,"syntology":null},{"url":"/paper/genomics-guided-representation-learning-for","slug":"genomics-guided-representation-learning-for","title":"Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction","date":"2024-06-10","arxiv_id":"2406.06517","repositories_listed":1,"syntology":null},{"url":"/paper/linear-causal-representation-learning-from","slug":"linear-causal-representation-learning-from","title":"Linear Causal Representation Learning from Unknown Multi-node Interventions","date":"2024-06-09","arxiv_id":"2406.05937","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/linear-causal-representation-learning-from#ran","syntology_url":"https://syntology.ai/paper/2406.05937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05937"}},"official":{"repos":["acarturk-e/umni-crl"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-distilled-disentangled-learning-for","slug":"self-distilled-disentangled-learning-for","title":"Self-Distilled Disentangled Learning for Counterfactual Prediction","date":"2024-06-09","arxiv_id":"2406.05855","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-with-conditional","slug":"representation-learning-with-conditional","title":"Representation Learning with Conditional Information Flow Maximization","date":"2024-06-08","arxiv_id":"2406.05510","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-size-generalization-in-graph-neural","slug":"enhancing-size-generalization-in-graph-neural","title":"Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning","date":"2024-06-07","arxiv_id":"2406.04601","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":13,"n_pointer_only":18,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/enhancing-size-generalization-in-graph-neural#ran","syntology_url":"https://syntology.ai/paper/2406.04601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04601"}},"official":{"repos":["graphminddartmouth/disgen"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":3,"ran_from_kinds":["official"]}}}],"record_sha256":"ce67ad07aed9640567e2ba920bd02d73a52b4500e12b4ae6f18c243aa87a3ca2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}