{"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/52","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":52,"pages_in_order":106,"rows_per_page":100,"rows":[5101,5200],"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/51","next":"/task/representation-learning/papers/53","papers":[{"url":null,"slug":"contrastive-representation-learning-helps","title":"Contrastive Representation Learning Helps Cross-institutional Knowledge Transfer: A Study in Pediatric Ventilation Management","date":"2025-01-23","arxiv_id":"2501.13587","repositories_listed":0,"syntology":null},{"url":"/paper/deep-modularity-networks-with-diversity","slug":"deep-modularity-networks-with-diversity","title":"Deep Modularity Networks with Diversity--Preserving Regularization","date":"2025-01-23","arxiv_id":"2501.13451","repositories_listed":0,"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":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","sample_list":"/paper/deep-modularity-networks-with-diversity#ran","syntology_url":"https://syntology.ai/paper/2501.13451","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.13451"}},"official":null}},{"url":null,"slug":"dq-data2vec-decoupling-quantization-for","title":"DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition","date":"2025-01-23","arxiv_id":"2501.13497","repositories_listed":0,"syntology":null},{"url":null,"slug":"exlm-rethinking-the-impact-of-texttt-mask","title":"ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models","date":"2025-01-23","arxiv_id":"2501.13397","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-learning-representations-for-tabular-data","title":"On Learning Representations for Tabular Data Distillation","date":"2025-01-23","arxiv_id":"2501.13905","repositories_listed":0,"syntology":null},{"url":null,"slug":"wasserstein-regularized-conformal-prediction","title":"Wasserstein-regularized Conformal Prediction under General Distribution Shift","date":"2025-01-23","arxiv_id":"2501.13430","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-with-diffusion","title":"Graph Representation Learning with Diffusion Generative Models","date":"2025-01-22","arxiv_id":"2501.13133","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierpromptlm-a-pure-plm-based-framework-for","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","date":"2025-01-22","arxiv_id":"2501.12857","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-masked-autoencoders-for-character","title":"Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification","date":"2025-01-21","arxiv_id":"2501.11895","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-multimodal-uncertainty","title":"High-dimensional multimodal uncertainty estimation by manifold alignment:Application to 3D right ventricular strain computations","date":"2025-01-21","arxiv_id":"2501.12178","repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-of-nonparametric-dynamic","title":"Identification of Nonparametric Dynamic Causal Structure and Latent Process in Climate System","date":"2025-01-21","arxiv_id":"2501.12500","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-blockchain-analysis-tackling","title":"Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks","date":"2025-01-21","arxiv_id":"2501.12491","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameterised-quantum-circuits-for-novel","title":"Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition","date":"2025-01-21","arxiv_id":"2501.12050","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-effective-digraph-representation","title":"Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach","date":"2025-01-21","arxiv_id":"2501.11817","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-intrinsic-rewards-on","title":"The impact of intrinsic rewards on exploration in Reinforcement Learning","date":"2025-01-20","arxiv_id":"2501.11533","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-transferable-homogeneous-groups-for","title":"Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning","date":"2025-01-18","arxiv_id":"2501.10695","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-binary-representation-learning-for","title":"Sparse Binary Representation Learning for Knowledge Tracing","date":"2025-01-17","arxiv_id":"2501.09893","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-the-trigger-causal-abductive","title":"Finding the Trigger: Causal Abductive Reasoning on Video Events","date":"2025-01-16","arxiv_id":"2501.09304","repositories_listed":0,"syntology":null},{"url":null,"slug":"metric-learning-with-progressive-self","title":"Metric Learning with Progressive Self-Distillation for Audio-Visual Embedding Learning","date":"2025-01-16","arxiv_id":"2501.09608","repositories_listed":0,"syntology":null},{"url":null,"slug":"strategic-base-representation-learning-via","title":"Strategic Base Representation Learning via Feature Augmentations for Few-Shot Class Incremental Learning","date":"2025-01-16","arxiv_id":"2501.09361","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-devil-is-in-the-details-simple-remedies","title":"The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning","date":"2025-01-16","arxiv_id":"2501.09485","repositories_listed":0,"syntology":null},{"url":null,"slug":"dnmdr-dynamic-networks-and-multi-view-drug","title":"DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation","date":"2025-01-15","arxiv_id":"2501.08572","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-aware-spatio-temporal-representation","title":"Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction","date":"2025-01-15","arxiv_id":"2501.09049","repositories_listed":0,"syntology":null},{"url":null,"slug":"magnet-augmenting-generative-decoders-with","title":"MAGNET: Augmenting Generative Decoders with Representation Learning and Infilling Capabilities","date":"2025-01-15","arxiv_id":"2501.08648","repositories_listed":0,"syntology":null},{"url":null,"slug":"texttt-infohier-hierarchical-information","title":"$\\texttt{InfoHier}$: Hierarchical Information Extraction via Encoding and Embedding","date":"2025-01-15","arxiv_id":"2501.08717","repositories_listed":0,"syntology":null},{"url":null,"slug":"accon-angle-compensated-contrastive","title":"ACCon: Angle-Compensated Contrastive Regularizer for Deep Regression","date":"2025-01-13","arxiv_id":"2501.07045","repositories_listed":0,"syntology":null},{"url":null,"slug":"duplex-dual-prototype-learning-for","title":"Duplex: Dual Prototype Learning for Compositional Zero-Shot Learning","date":"2025-01-13","arxiv_id":"2501.07114","repositories_listed":0,"syntology":null},{"url":null,"slug":"localization-aware-multi-scale-representation","title":"Localization-Aware Multi-Scale Representation Learning for Repetitive Action Counting","date":"2025-01-13","arxiv_id":"2501.07312","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-of-point-cloud","title":"Representation Learning of Point Cloud Upsampling in Global and Local Inputs","date":"2025-01-13","arxiv_id":"2501.07076","repositories_listed":0,"syntology":null},{"url":null,"slug":"subject-representation-learning-from-eeg","title":"Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders","date":"2025-01-13","arxiv_id":"2501.16626","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-modality-representation-learning-for","title":"Dual-Modality Representation Learning for Molecular Property Prediction","date":"2025-01-11","arxiv_id":"2501.06608","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-path-planning-performance-through","title":"Enhancing Path Planning Performance through Image Representation Learning of High-Dimensional Configuration Spaces","date":"2025-01-11","arxiv_id":"2501.06639","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-factorizing-and-disentangling-a","title":"Multi-View Factorizing and Disentangling: A Novel Framework for Incomplete Multi-View Multi-Label Classification","date":"2025-01-11","arxiv_id":"2501.06524","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-supervision-for-low-light","title":"Natural Language Supervision for Low-light Image Enhancement","date":"2025-01-11","arxiv_id":"2501.06546","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-text-based-knowledge-embedded-soft-sensing","title":"A Text-Based Knowledge-Embedded Soft Sensing Modeling Approach for General Industrial Process Tasks Based on Large Language Model","date":"2025-01-09","arxiv_id":"2501.05075","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedsa-a-unified-representation-learning-via","title":"FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning","date":"2025-01-09","arxiv_id":"2501.05496","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-spectral-graph-neural-networks","title":"Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Report","date":"2025-01-08","arxiv_id":"2501.04570","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-generalizable-trajectory-prediction","title":"Towards Generalizable Trajectory Prediction Using Dual-Level Representation Learning And Adaptive Prompting","date":"2025-01-08","arxiv_id":"2501.04815","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-within-tabular-data-foundations","title":"Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions","date":"2025-01-07","arxiv_id":"2501.03540","repositories_listed":0,"syntology":null},{"url":null,"slug":"largead-large-scale-cross-sensor-data","title":"LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving","date":"2025-01-07","arxiv_id":"2501.04005","repositories_listed":0,"syntology":null},{"url":null,"slug":"modality-invariant-bidirectional-temporal","title":"Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis","date":"2025-01-07","arxiv_id":"2501.05474","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-supply-chain-networks-with-the","title":"Optimizing Supply Chain Networks with the Power of Graph Neural Networks","date":"2025-01-07","arxiv_id":"2501.06221","repositories_listed":0,"syntology":null},{"url":null,"slug":"semise-semi-supervised-learning-for-severity","title":"Semise: Semi-supervised learning for severity representation in medical image","date":"2025-01-07","arxiv_id":"2501.03848","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-masked-autoencoders","title":"Gaussian Masked Autoencoders","date":"2025-01-06","arxiv_id":"2501.03229","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-gaze-boosts-object-centered","title":"Human Gaze Boosts Object-Centered Representation Learning","date":"2025-01-06","arxiv_id":"2501.02966","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeing-the-whole-in-the-parts-in-self","title":"Seeing the Whole in the Parts in Self-Supervised Representation Learning","date":"2025-01-06","arxiv_id":"2501.02860","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedrsclip-federated-learning-for-remote","title":"FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models","date":"2025-01-05","arxiv_id":"2501.02461","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-load-forecasting-via","title":"Interpretable Load Forecasting via Representation Learning of Geo-distributed Meteorological Factors","date":"2025-01-04","arxiv_id":"2501.02241","repositories_listed":0,"syntology":null},{"url":null,"slug":"remodeling-peptide-mhc-tcr-triad-binding-as","title":"Remodeling Peptide-MHC-TCR Triad Binding as Sequence Fusion for Immunogenicity Prediction","date":"2025-01-03","arxiv_id":"2501.01768","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-discrete-encoders-identifiable-deep","title":"Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers","date":"2025-01-02","arxiv_id":"2501.01414","repositories_listed":0,"syntology":null},{"url":null,"slug":"drift2matrix-kernel-induced-self","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","date":"2025-01-02","arxiv_id":"2501.01480","repositories_listed":0,"syntology":null},{"url":null,"slug":"kans-knowledge-discovery-graph-attention","title":"KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes","date":"2025-01-02","arxiv_id":"2501.02015","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-part-learning-for-fine-grained","title":"Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bg-triangle-bezier-gaussian-triangle-for-3d-1","title":"BG-Triangle: Bezier Gaussian Triangle for 3D Vectorization and Rendering","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"boe-vit-boosting-orientation-estimation-with","title":"BOE-ViT: Boosting Orientation Estimation with Equivariance in Self-Supervised 3D Subtomogram Alignment","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"breaking-the-memory-barrier-of-contrastive","title":"Breaking the Memory Barrier of Contrastive Loss via Tile-Based Strategy","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-3d-representation-with-multi-view","title":"Cross-Modal 3D Representation with Multi-View Images and Point Clouds","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"de-2gaze-deformable-and-decoupled","title":"De^2Gaze: Deformable and Decoupled Representation Learning for 3D Gaze Estimation","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-then-progressive-fusion-with-view","title":"Enhanced then Progressive Fusion with View Graph for Multi-View Clustering","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"freqdebias-towards-generalizable-deepfake","title":"FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-splatting-feature-fields-for-privacy","title":"Gaussian Splatting Feature Fields for (Privacy-Preserving) Visual Localization","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-skeleton-based-action","title":"Heterogeneous Skeleton-Based Action Representation Learning","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-compact-clustering-attention-1","title":"Hierarchical Compact Clustering Attention (COCA) for Unsupervised Object-Centric Learning","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kan-kan-buff-signed-graph-neural-networks","title":"KAN KAN Buff Signed Graph Neural Networks?","date":"2025-01-01","arxiv_id":"2501.00709","repositories_listed":0,"syntology":null},{"url":null,"slug":"lidargait-learning-local-features-and-size","title":"LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"perceptual-inductive-bias-is-what-you-need","title":"Perceptual Inductive Bias Is What You Need Before Contrastive Learning","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semidavil-semi-supervised-domain-adaptation","title":"SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"seqmvrl-a-sequential-fusion-framework-for","title":"SeqMvRL: A Sequential Fusion Framework for Multi-view Representation Learning","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"soma-singular-value-decomposed-minor","title":"SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-state-space-model-for-rotation-1","title":"Spectral State Space Model for Rotation-Invariant Visual Representation Learning","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"star-with-bilinear-mapping","title":"Star with Bilinear Mapping","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"viewpoint-rosetta-stone-unlocking-unpaired","title":"Viewpoint Rosetta Stone: Unlocking Unpaired Ego-Exo Videos for View-invariant Representation Learning","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-representation-learning-through-causal","title":"Visual Representation Learning through Causal Intervention for Controllable Image Editing","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"when-domain-generalization-meets-generalized","title":"When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neurosleepnet-a-multi-head-self-attention","title":"NeuroSleepNet: A Multi-Head Self-Attention Based Automatic Sleep Scoring Scheme with Spatial and Multi-Scale Temporal Representation Learning","date":"2024-12-31","arxiv_id":"2501.00557","repositories_listed":0,"syntology":null},{"url":null,"slug":"per-subject-complexity-in-eye-movement","title":"Gaze Prediction as a Function of Eye Movement Type and Individual Differences","date":"2024-12-31","arxiv_id":"2501.00597","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-variational-autoencoder-a","title":"Multimodal Variational Autoencoder: a Barycentric View","date":"2024-12-29","arxiv_id":"2412.20487","repositories_listed":0,"syntology":null},{"url":null,"slug":"bird-vocalization-embedding-extraction-using","title":"Bird Vocalization Embedding Extraction Using Self-Supervised Disentangled Representation Learning","date":"2024-12-28","arxiv_id":"2412.20146","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-contrastive-meta-learning","title":"Transformer-Based Contrastive Meta-Learning For Low-Resource Generalizable Activity Recognition","date":"2024-12-28","arxiv_id":"2412.20290","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-fine-tuning-and-re-ranking-a","title":"Pre-training, Fine-tuning and Re-ranking: A Three-Stage Framework for Legal Question Answering","date":"2024-12-27","arxiv_id":"2412.19482","repositories_listed":0,"syntology":null},{"url":null,"slug":"clip-gs-unifying-vision-language","title":"CLIP-GS: Unifying Vision-Language Representation with 3D Gaussian Splatting","date":"2024-12-26","arxiv_id":"2412.19142","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-better-spherical-sliced-wasserstein","title":"Towards Better Spherical Sliced-Wasserstein Distance Learning with Data-Adaptive Discriminative Projection Direction","date":"2024-12-26","arxiv_id":"2412.19212","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-self-supervised-learning-for-social","title":"Automatic Self-supervised Learning for Social Recommendations","date":"2024-12-25","arxiv_id":"2412.18735","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-and-lightweight-representation","title":"Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs","date":"2024-12-25","arxiv_id":"2412.18720","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-dynamic-duo-of-collaborative-masking-and","title":"The Dynamic Duo of Collaborative Masking and Target for Advanced Masked Autoencoder Learning","date":"2024-12-23","arxiv_id":"2412.17566","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-pixels-to-gigapixels-bridging-local","title":"From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba","date":"2024-12-21","arxiv_id":"2412.16711","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-latent-variable-modeling-for","title":"Probabilistic Latent Variable Modeling for Dynamic Friction Identification and Estimation","date":"2024-12-20","arxiv_id":"2412.15756","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-visual-composition-through-improved","title":"Learning Visual Composition through Improved Semantic Guidance","date":"2024-12-19","arxiv_id":"2412.15396","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-color-channel-independence-for","title":"Leveraging Color Channel Independence for Improved Unsupervised Object Detection","date":"2024-12-19","arxiv_id":"2412.15150","repositories_listed":0,"syntology":null},{"url":null,"slug":"st-rep-learning-predictive-representations","title":"ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting","date":"2024-12-19","arxiv_id":"2412.14537","repositories_listed":0,"syntology":null},{"url":null,"slug":"treatment-effects-estimation-on-networked","title":"Treatment Effects Estimation on Networked Observational Data using Disentangled Variational Graph Autoencoder","date":"2024-12-19","arxiv_id":"2412.14497","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-guided-medical-image-segmentation","title":"Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments","date":"2024-12-18","arxiv_id":"2412.13533","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-speech-command-recognition","title":"Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation","date":"2024-12-17","arxiv_id":"2412.12858","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-spring-neural-odes-for-link-sign","title":"Graph Spring Neural ODEs for Link Sign Prediction","date":"2024-12-17","arxiv_id":"2412.12916","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-set-heterogeneous-domain-adaptation","title":"Open-Set Heterogeneous Domain Adaptation: Theoretical Analysis and Algorithm","date":"2024-12-17","arxiv_id":"2412.13036","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-physically-interpretable-world-models","title":"Towards Physically Interpretable World Models: Meaningful Weakly Supervised Representations for Visual Trajectory Prediction","date":"2024-12-17","arxiv_id":"2412.12870","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-object-centric-representation","title":"Efficient Object-centric Representation Learning with Pre-trained Geometric Prior","date":"2024-12-16","arxiv_id":"2412.12331","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-analysis-for-deep-contrastive","title":"Generalization Analysis for Deep Contrastive Representation Learning","date":"2024-12-16","arxiv_id":"2412.12014","repositories_listed":0,"syntology":null},{"url":null,"slug":"scam-detection-for-ethereum-smart-contracts","title":"Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain","date":"2024-12-16","arxiv_id":"2412.12370","repositories_listed":0,"syntology":null},{"url":null,"slug":"se-gcl-an-event-based-simple-and-effective","title":"SE-GCL: An Event-Based Simple and Effective Graph Contrastive Learning for Text Representation","date":"2024-12-16","arxiv_id":"2412.11652","repositories_listed":0,"syntology":null}],"record_sha256":"840644a7c86652d137bb171e46ee722dbdea05c6f74dda46f37288badaa24d7a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}