{"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/55","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":55,"pages_in_order":106,"rows_per_page":100,"rows":[5401,5500],"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/54","next":"/task/representation-learning/papers/56","papers":[{"url":null,"slug":"the-optimization-landscape-of-sgd-across-the","title":"The Optimization Landscape of SGD Across the Feature Learning Strength","date":"2024-10-06","arxiv_id":"2410.04642","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-fair-representation-learning-for","title":"Rethinking Fair Representation Learning for Performance-Sensitive Tasks","date":"2024-10-05","arxiv_id":"2410.04120","repositories_listed":0,"syntology":null},{"url":null,"slug":"cognitive-maps-and-schizophrenia","title":"Cognitive maps and schizophrenia","date":"2024-10-04","arxiv_id":"2410.03510","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-node-representation-by-boosting","title":"Improving Node Representation by Boosting Target-Aware Contrastive Loss","date":"2024-10-04","arxiv_id":"2410.03901","repositories_listed":0,"syntology":null},{"url":null,"slug":"vedit-latent-prediction-architecture-for","title":"VEDIT: Latent Prediction Architecture For Procedural Video Representation Learning","date":"2024-10-04","arxiv_id":"2410.03478","repositories_listed":0,"syntology":null},{"url":null,"slug":"classcontrast-bridging-the-spatial-and","title":"ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations","date":"2024-10-03","arxiv_id":"2410.02158","repositories_listed":0,"syntology":null},{"url":null,"slug":"collap-contrastive-long-form-language-audio","title":"CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation","date":"2024-10-03","arxiv_id":"2410.02271","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-representation-learning-for-5","title":"Disentangled Representation Learning for Parametric Partial Differential Equations","date":"2024-10-03","arxiv_id":"2410.02136","repositories_listed":0,"syntology":null},{"url":null,"slug":"repurposing-foundation-model-for","title":"Repurposing Foundation Model for Generalizable Medical Time Series Classification","date":"2024-10-03","arxiv_id":"2410.03794","repositories_listed":0,"syntology":null},{"url":null,"slug":"trajgpt-irregular-time-series-representation","title":"TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis","date":"2024-10-03","arxiv_id":"2410.02133","repositories_listed":0,"syntology":null},{"url":null,"slug":"david-domain-adaptive-visually-rich-document","title":"DAViD: Domain Adaptive Visually-Rich Document Understanding with Synthetic Insights","date":"2024-10-02","arxiv_id":"2410.01609","repositories_listed":0,"syntology":null},{"url":null,"slug":"decorrelation-based-self-supervised-visual","title":"Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification","date":"2024-10-02","arxiv_id":"2410.01441","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-with-a-joint-embedding-predictive","title":"Denoising with a Joint-Embedding Predictive Architecture","date":"2024-10-02","arxiv_id":"2410.03755","repositories_listed":0,"syntology":null},{"url":null,"slug":"farm-functional-group-aware-representations","title":"FARM: Functional Group-Aware Representations for Small Molecules","date":"2024-10-02","arxiv_id":"2410.02082","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-sample-efficient-multi-task","title":"Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits","date":"2024-10-02","arxiv_id":"2410.02068","repositories_listed":0,"syntology":null},{"url":null,"slug":"jamming-detection-in-mimo-ofdm-isac-systems","title":"Jamming Detection in MIMO-OFDM ISAC Systems Using Variational Autoencoders","date":"2024-10-02","arxiv_id":"2410.01632","repositories_listed":0,"syntology":null},{"url":null,"slug":"lagem-a-large-geometry-model-for-3d","title":"LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusion","date":"2024-10-02","arxiv_id":"2410.01295","repositories_listed":0,"syntology":null},{"url":null,"slug":"score-based-pullback-riemannian-geometry","title":"Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows","date":"2024-10-02","arxiv_id":"2410.01950","repositories_listed":0,"syntology":null},{"url":null,"slug":"toper-topological-embeddings-in-graph","title":"TopER: Topological Embeddings in Graph Representation Learning","date":"2024-10-02","arxiv_id":"2410.01778","repositories_listed":0,"syntology":null},{"url":null,"slug":"verbalized-graph-representation-learning-a","title":"Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process","date":"2024-10-02","arxiv_id":"2410.01457","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-medical-radiograph-representation","title":"Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity","date":"2024-10-01","arxiv_id":"2410.00448","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-representation-learning-with","title":"Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments","date":"2024-10-01","arxiv_id":"2410.00903","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-representation-learning-for-4","title":"Contrastive Representation Learning for Predicting Solar Flares from Extremely Imbalanced Multivariate Time Series Data","date":"2024-10-01","arxiv_id":"2410.00312","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-to-global-self-supervised","title":"Local-to-Global Self-Supervised Representation Learning for Diabetic Retinopathy Grading","date":"2024-10-01","arxiv_id":"2410.00779","repositories_listed":0,"syntology":null},{"url":null,"slug":"ngpt-normalized-transformer-with","title":"nGPT: Normalized Transformer with Representation Learning on the Hypersphere","date":"2024-10-01","arxiv_id":"2410.01131","repositories_listed":0,"syntology":null},{"url":null,"slug":"timesync-temporal-intent-modelling-with","title":"TIMeSynC: Temporal Intent Modelling with Synchronized Context Encodings for Financial Service Applications","date":"2024-10-01","arxiv_id":"2410.12825","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangling-singlish-discourse-particles","title":"Disentangling Singlish Discourse Particles with Task-Driven Representation","date":"2024-09-30","arxiv_id":"2409.20366","repositories_listed":0,"syntology":null},{"url":null,"slug":"possible-principles-for-aligned-structure","title":"Possible principles for aligned structure learning agents","date":"2024-09-30","arxiv_id":"2410.00258","repositories_listed":0,"syntology":null},{"url":null,"slug":"survival-prediction-in-lung-cancer-through","title":"Survival Prediction in Lung Cancer through Multi-Modal Representation Learning","date":"2024-09-30","arxiv_id":"2409.20179","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-medical-image-representation","title":"Universal Medical Image Representation Learning with Compositional Decoders","date":"2024-09-30","arxiv_id":"2409.19890","repositories_listed":0,"syntology":null},{"url":null,"slug":"fcop-focal-length-estimation-from-category","title":"fCOP: Focal Length Estimation from Category-level Object Priors","date":"2024-09-29","arxiv_id":"2409.19641","repositories_listed":0,"syntology":null},{"url":null,"slug":"focus-on-what-matters-separated-models-for","title":"Focus On What Matters: Separated Models For Visual-Based RL Generalization","date":"2024-09-29","arxiv_id":"2410.10834","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalized-model-for-multidimensional","title":"A Generalized Model for Multidimensional Intransitivity","date":"2024-09-28","arxiv_id":"2409.19325","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-euclidean-dual-space-representation","title":"Beyond Euclidean: Dual-Space Representation Learning for Weakly Supervised Video Violence Detection","date":"2024-09-28","arxiv_id":"2409.19252","repositories_listed":0,"syntology":null},{"url":null,"slug":"canonical-correlation-guided-deep-neural","title":"Canonical Correlation Guided Deep Neural Network","date":"2024-09-28","arxiv_id":"2409.19396","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-ground-level-image-and-remote","title":"Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery","date":"2024-09-28","arxiv_id":"2409.19439","repositories_listed":0,"syntology":null},{"url":null,"slug":"hstfl-a-heterogeneous-federated-learning","title":"HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting","date":"2024-09-27","arxiv_id":"2409.18482","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-representation-learning-for-multimodal","title":"Latent Representation Learning for Multimodal Brain Activity Translation","date":"2024-09-27","arxiv_id":"2409.18462","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-spatial-augmentation-in-self","title":"Analysis of Spatial augmentation in Self-supervised models in the purview of training and test distributions","date":"2024-09-26","arxiv_id":"2409.18228","repositories_listed":0,"syntology":null},{"url":null,"slug":"drl-stnet-unsupervised-domain-adaptation-for","title":"DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning","date":"2024-09-26","arxiv_id":"2409.18340","repositories_listed":0,"syntology":null},{"url":"/paper/efficient-fairness-performance-pareto-front","slug":"efficient-fairness-performance-pareto-front","title":"Efficient Fairness-Performance Pareto Front Computation","date":"2024-09-26","arxiv_id":"2409.17643","repositories_listed":0,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/efficient-fairness-performance-pareto-front#ran","syntology_url":"https://syntology.ai/paper/2409.17643","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17643"}},"official":null}},{"url":null,"slug":"heterogeneous-hyper-graph-neural-networks-for","title":"Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition","date":"2024-09-26","arxiv_id":"2409.17483","repositories_listed":0,"syntology":null},{"url":null,"slug":"muse-integrating-multi-knowledge-for","title":"MUSE: Integrating Multi-Knowledge for Knowledge Graph Completion","date":"2024-09-26","arxiv_id":"2409.17536","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-learning-on-cell-embedded","title":"Spatiotemporal Learning on Cell-embedded Graphs","date":"2024-09-26","arxiv_id":"2409.18013","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-prompting-based-representation-learning","title":"A Prompting-Based Representation Learning Method for Recommendation with Large Language Models","date":"2024-09-25","arxiv_id":"2409.16674","repositories_listed":0,"syntology":null},{"url":null,"slug":"demo2vec-learning-region-embedding-with","title":"Demo2Vec: Learning Region Embedding with Demographic Information","date":"2024-09-25","arxiv_id":"2409.16837","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-effect-of-perceptual-metrics-on-music","title":"The Effect of Perceptual Metrics on Music Representation Learning for Genre Classification","date":"2024-09-25","arxiv_id":"2409.17069","repositories_listed":0,"syntology":null},{"url":null,"slug":"trading-through-earnings-seasons-using-self","title":"Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning","date":"2024-09-25","arxiv_id":"2409.17392","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-random-codebooks-for-audio-neural","title":"Using Random Codebooks for Audio Neural AutoEncoders","date":"2024-09-25","arxiv_id":"2409.16677","repositories_listed":0,"syntology":null},{"url":"/paper/3d-jepa-a-joint-embedding-predictive","slug":"3d-jepa-a-joint-embedding-predictive","title":"3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning","date":"2024-09-24","arxiv_id":"2409.15803","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangling-age-and-identity-with-a-mutual","title":"Disentangling Age and Identity with a Mutual Information Minimization Approach for Cross-Age Speaker Verification","date":"2024-09-24","arxiv_id":"2409.15974","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperbolic-image-and-pointcloud-contrastive","title":"Hyperbolic Image-and-Pointcloud Contrastive Learning for 3D Classification","date":"2024-09-24","arxiv_id":"2409.15810","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-world-object-detection-with-instance","title":"OW-Rep: Open World Object Detection with Instance Representation Learning","date":"2024-09-24","arxiv_id":"2409.16073","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-representation-learning-with-6","title":"Self-Supervised Representation Learning with Augmentations of Continuous Training Data Improves the Feel and Performance of Myoelectric Control","date":"2024-09-24","arxiv_id":"2409.16015","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-text-representation-learning-via","title":"Unsupervised Text Representation Learning via Instruction-Tuning for Zero-Shot Dense Retrieval","date":"2024-09-24","arxiv_id":"2409.16497","repositories_listed":0,"syntology":null},{"url":null,"slug":"2409-17909","title":"Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment","date":"2024-09-23","arxiv_id":"2409.17909","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-learning-on-user-segmentation","title":"Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction","date":"2024-09-23","arxiv_id":"2409.14945","repositories_listed":0,"syntology":null},{"url":null,"slug":"causkelnet-causal-representation-learning-for","title":"CauSkelNet: Causal Representation Learning for Human Behaviour Analysis","date":"2024-09-23","arxiv_id":"2409.15564","repositories_listed":0,"syntology":null},{"url":null,"slug":"kriformer-a-novel-spatiotemporal-kriging","title":"Kriformer: A Novel Spatiotemporal Kriging Approach Based on Graph Transformers","date":"2024-09-23","arxiv_id":"2409.14906","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-feature-transformation-for","title":"Reinforcement Feature Transformation for Polymer Property Performance Prediction","date":"2024-09-23","arxiv_id":"2409.15616","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-music-foundation-models-better-at-singing","title":"Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models","date":"2024-09-21","arxiv_id":"2409.14131","repositories_listed":0,"syntology":null},{"url":null,"slug":"finemoltex-towards-fine-grained-molecular","title":"FineMolTex: Towards Fine-grained Molecular Graph-Text Pre-training","date":"2024-09-21","arxiv_id":"2409.14106","repositories_listed":0,"syntology":null},{"url":null,"slug":"formula-supervised-visual-geometric-pre","title":"Formula-Supervised Visual-Geometric Pre-training","date":"2024-09-20","arxiv_id":"2409.13535","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-salient-object-detection-on-compressed","title":"Robust Salient Object Detection on Compressed Images Using Convolutional Neural Networks","date":"2024-09-20","arxiv_id":"2409.13464","repositories_listed":0,"syntology":null},{"url":null,"slug":"wormhole-concept-aware-deep-representation","title":"Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences","date":"2024-09-20","arxiv_id":"2409.13857","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multi-manifold-embedding-for-out-of","title":"Learning Multi-Manifold Embedding for Out-Of-Distribution Detection","date":"2024-09-19","arxiv_id":"2409.12479","repositories_listed":0,"syntology":null},{"url":null,"slug":"deteclap-enhancing-audio-visual","title":"DETECLAP: Enhancing Audio-Visual Representation Learning with Object Information","date":"2024-09-18","arxiv_id":"2409.11729","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-relational-embeddings","title":"Geometric Relational Embeddings","date":"2024-09-18","arxiv_id":"2409.15369","repositories_listed":0,"syntology":null},{"url":null,"slug":"imrl-integrating-visual-physical-temporal-and","title":"IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition","date":"2024-09-18","arxiv_id":"2409.12092","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-mechanistic-models-of-event","title":"A Review of Mechanistic Models of Event Comprehension","date":"2024-09-17","arxiv_id":"2409.18992","repositories_listed":0,"syntology":null},{"url":null,"slug":"augment-drop-swap-improving-diversity-in-llm","title":"Augment, Drop & Swap: Improving Diversity in LLM Captions for Efficient Music-Text Representation Learning","date":"2024-09-17","arxiv_id":"2409.11498","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-graph-pooling-based-on-minimum","title":"MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length","date":"2024-09-16","arxiv_id":"2409.10263","repositories_listed":0,"syntology":null},{"url":null,"slug":"jina-embeddings-v3-multilingual-embeddings","title":"jina-embeddings-v3: Multilingual Embeddings With Task LoRA","date":"2024-09-16","arxiv_id":"2409.10173","repositories_listed":0,"syntology":null},{"url":"/paper/macdiff-unified-skeleton-modeling-with-masked","slug":"macdiff-unified-skeleton-modeling-with-masked","title":"MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion","date":"2024-09-16","arxiv_id":"2409.10473","repositories_listed":0,"syntology":{"n":12,"n_ran":8,"n_constructed":6,"n_ran_checked":7,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":12,"phrase":"8 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/macdiff-unified-skeleton-modeling-with-masked#ran","syntology_url":"https://syntology.ai/paper/2409.10473","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.10473"}},"official":null}},{"url":null,"slug":"enhancing-weakly-supervised-object-detection","title":"Enhancing Weakly-Supervised Object Detection on Static Images through (Hallucinated) Motion","date":"2024-09-15","arxiv_id":"2409.09616","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-multimodal-speech","title":"Self-supervised Multimodal Speech Representations for the Assessment of Schizophrenia Symptoms","date":"2024-09-15","arxiv_id":"2409.09733","repositories_listed":0,"syntology":null},{"url":null,"slug":"turbo-your-multi-modal-classification-with","title":"Turbo your multi-modal classification with contrastive learning","date":"2024-09-14","arxiv_id":"2409.09282","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-image-reconstruction-and-segmentation-1","title":"Joint image reconstruction and segmentation of real-time cardiac MRI in free-breathing using a model based on disentangled representation learning","date":"2024-09-13","arxiv_id":"2409.08619","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-unsupervised-dialogue-topic-segmentation","title":"An Unsupervised Dialogue Topic Segmentation Model Based on Utterance Rewriting","date":"2024-09-12","arxiv_id":"2409.07672","repositories_listed":0,"syntology":null},{"url":null,"slug":"gre-2-mdcl-graph-representation-embedding","title":"GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning","date":"2024-09-12","arxiv_id":"2409.07725","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-object-event-graph-representation","title":"Multi-object event graph representation learning for Video Question Answering","date":"2024-09-12","arxiv_id":"2409.07747","repositories_listed":0,"syntology":null},{"url":null,"slug":"top-down-activity-representation-learning-for","title":"Top-down Activity Representation Learning for Video Question Answering","date":"2024-09-12","arxiv_id":"2409.07748","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-domain-gap-of-point-cloud","title":"Bridging Domain Gap of Point Cloud Representations via Self-Supervised Geometric Augmentation","date":"2024-09-11","arxiv_id":"2409.06956","repositories_listed":0,"syntology":null},{"url":null,"slug":"current-symmetry-group-equivariant","title":"Current Symmetry Group Equivariant Convolution Frameworks for Representation Learning","date":"2024-09-11","arxiv_id":"2409.07327","repositories_listed":0,"syntology":null},{"url":null,"slug":"bottleneck-based-encoder-decoder-architecture","title":"Bottleneck-based Encoder-decoder ARchitecture (BEAR) for Learning Unbiased Consumer-to-Consumer Image Representations","date":"2024-09-10","arxiv_id":"2409.06187","repositories_listed":0,"syntology":null},{"url":null,"slug":"intra-interaction-relationship-aware-weakly","title":"INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding","date":"2024-09-10","arxiv_id":"2409.06210","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapted-moe-mixture-of-experts-with-test-time","title":"Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection","date":"2024-09-09","arxiv_id":"2409.05611","repositories_listed":0,"syntology":null},{"url":null,"slug":"ethereum-fraud-detection-via-joint","title":"Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning","date":"2024-09-09","arxiv_id":"2409.07494","repositories_listed":0,"syntology":null},{"url":null,"slug":"graffin-stand-for-tails-in-imbalanced-node","title":"Graffin: Stand for Tails in Imbalanced Node Classification","date":"2024-09-09","arxiv_id":"2409.05339","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-few-shot-classification-with-semi","title":"Large-Scale Few-Shot Classification with Semi-supervised Hierarchical k-Probabilistic PCAs","date":"2024-09-09","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mtlso-a-multi-task-learning-approach-for","title":"MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization","date":"2024-09-09","arxiv_id":"2409.06077","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-world-dynamic-prompt-and-continual","title":"Open-World Dynamic Prompt and Continual Visual Representation Learning","date":"2024-09-09","arxiv_id":"2409.05312","repositories_listed":0,"syntology":null},{"url":null,"slug":"sgc-vqgan-towards-complex-scene","title":"SGC-VQGAN: Towards Complex Scene Representation via Semantic Guided Clustering Codebook","date":"2024-09-09","arxiv_id":"2409.06105","repositories_listed":0,"syntology":null},{"url":null,"slug":"gencad-image-conditioned-computer-aided","title":"GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors","date":"2024-09-08","arxiv_id":"2409.16294","repositories_listed":0,"syntology":null},{"url":null,"slug":"icml-topological-deep-learning-challenge-2024","title":"ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain","date":"2024-09-08","arxiv_id":"2409.05211","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-scenario-attention-based-generative","title":"A Multi-scenario Attention-based Generative Model for Personalized Blood Pressure Time Series Forecasting","date":"2024-09-07","arxiv_id":"2409.04704","repositories_listed":0,"syntology":null},{"url":null,"slug":"constrained-multi-layer-contrastive-learning","title":"Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition","date":"2024-09-07","arxiv_id":"2409.13716","repositories_listed":0,"syntology":null},{"url":null,"slug":"granular-ball-representation-learning-for","title":"Granular-ball Representation Learning for Deep CNN on Learning with Label Noise","date":"2024-09-05","arxiv_id":"2409.03254","repositories_listed":0,"syntology":null},{"url":null,"slug":"organized-grouped-discrete-representation-for","title":"Organized Grouped Discrete Representation for Object-Centric Learning","date":"2024-09-05","arxiv_id":"2409.03553","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-we-trust-what-they-say-or-what-they-do-a","title":"Do We Trust What They Say or What They Do? A Multimodal User Embedding Provides Personalized Explanations","date":"2024-09-04","arxiv_id":"2409.02965","repositories_listed":0,"syntology":null}],"record_sha256":"8935a2f9c92aa6ae504f5bd3b6bff81300b43ade71759fb88022a7d2f86c95b9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}