{"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/60","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":60,"pages_in_order":106,"rows_per_page":100,"rows":[5901,6000],"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/59","next":"/task/representation-learning/papers/61","papers":[{"url":null,"slug":"leveraging-fine-grained-information-and-noise","title":"Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection","date":"2024-04-17","arxiv_id":"2404.11318","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-driven-feature-diffusion-for-open","title":"Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning","date":"2024-04-17","arxiv_id":"2404.11795","repositories_listed":0,"syntology":null},{"url":null,"slug":"aghint-attribute-guided-representation","title":"AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer","date":"2024-04-16","arxiv_id":"2404.10443","repositories_listed":0,"syntology":null},{"url":null,"slug":"higraphdti-hierarchical-graph-representation","title":"HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction","date":"2024-04-16","arxiv_id":"2404.10561","repositories_listed":0,"syntology":null},{"url":null,"slug":"residual-connections-harm-self-supervised","title":"Residual Connections Harm Generative Representation Learning","date":"2024-04-16","arxiv_id":"2404.10947","repositories_listed":0,"syntology":null},{"url":null,"slug":"neighbour-level-message-interaction-encoding","title":"Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs","date":"2024-04-15","arxiv_id":"2404.09809","repositories_listed":0,"syntology":null},{"url":null,"slug":"randalign-a-parameter-free-method-for","title":"RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks","date":"2024-04-15","arxiv_id":"2404.09774","repositories_listed":0,"syntology":null},{"url":null,"slug":"utility-fairness-trade-offs-and-how-to-find","title":"Utility-Fairness Trade-Offs and How to Find Them","date":"2024-04-15","arxiv_id":"2404.09454","repositories_listed":0,"syntology":null},{"url":null,"slug":"gcc-generative-calibration-clustering","title":"GCC: Generative Calibration Clustering","date":"2024-04-14","arxiv_id":"2404.09115","repositories_listed":0,"syntology":null},{"url":null,"slug":"ronid-new-intent-discovery-with-generated","title":"RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations","date":"2024-04-13","arxiv_id":"2404.08977","repositories_listed":0,"syntology":null},{"url":null,"slug":"aimdit-modality-augmentation-and-interaction","title":"AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations","date":"2024-04-12","arxiv_id":"2407.00743","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-cascading-effects-in-large","title":"Mitigating Cascading Effects in Large Adversarial Graph Environments","date":"2024-04-12","arxiv_id":"2404.14418","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectralmamba-efficient-mamba-for","title":"SpectralMamba: Efficient Mamba for Hyperspectral Image Classification","date":"2024-04-12","arxiv_id":"2404.08489","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-contrastive-learning-refine-embeddings","title":"Can Contrastive Learning Refine Embeddings","date":"2024-04-11","arxiv_id":"2404.08701","repositories_listed":0,"syntology":null},{"url":null,"slug":"pathology-genomic-fusion-via-biologically","title":"Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis","date":"2024-04-11","arxiv_id":"2404.08023","repositories_listed":0,"syntology":null},{"url":null,"slug":"vetrass-vehicle-trajectory-similarity-search","title":"VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and Representation Learning","date":"2024-04-11","arxiv_id":"2404.08021","repositories_listed":0,"syntology":null},{"url":null,"slug":"latim-longitudinal-representation-learning-in","title":"LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression","date":"2024-04-10","arxiv_id":"2404.07091","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-representation-learning-for-multi","title":"Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population","date":"2024-04-08","arxiv_id":"2404.05613","repositories_listed":0,"syntology":null},{"url":null,"slug":"social-mae-social-masked-autoencoder-for","title":"Social-MAE: Social Masked Autoencoder for Multi-person Motion Representation Learning","date":"2024-04-08","arxiv_id":"2404.05578","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-clinical-oriented-multi-level-contrastive","title":"A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images","date":"2024-04-07","arxiv_id":"2404.04887","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-for-binary-programming","title":"Graph Neural Networks for Binary Programming","date":"2024-04-07","arxiv_id":"2404.04874","repositories_listed":0,"syntology":null},{"url":null,"slug":"havtr-improving-video-text-retrieval-through","title":"HaVTR: Improving Video-Text Retrieval Through Augmentation Using Large Foundation Models","date":"2024-04-07","arxiv_id":"2404.05083","repositories_listed":0,"syntology":null},{"url":null,"slug":"skill-transfer-and-discovery-for-sim-to-real","title":"Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint","date":"2024-04-07","arxiv_id":"2404.05051","repositories_listed":0,"syntology":null},{"url":null,"slug":"timecsl-unsupervised-contrastive-learning-of","title":"TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis","date":"2024-04-07","arxiv_id":"2404.05057","repositories_listed":0,"syntology":null},{"url":null,"slug":"tcan-text-oriented-cross-attention-network","title":"TCAN: Text-oriented Cross Attention Network for Multimodal Sentiment Analysis","date":"2024-04-06","arxiv_id":"2404.04545","repositories_listed":0,"syntology":null},{"url":null,"slug":"jobformer-skill-aware-job-recommendation-with","title":"JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer","date":"2024-04-05","arxiv_id":"2404.04313","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-heterogeneity-in-federated","title":"Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity","date":"2024-04-05","arxiv_id":"2404.03854","repositories_listed":0,"syntology":null},{"url":null,"slug":"csr-dmri-continuous-super-resolution-of","title":"CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning","date":"2024-04-04","arxiv_id":"2404.03209","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-privacy-funnel-model-from-a","title":"Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition","date":"2024-04-03","arxiv_id":"2404.02696","repositories_listed":0,"syntology":null},{"url":null,"slug":"promptcodec-high-fidelity-neural-speech-codec","title":"PSCodec: A Series of High-Fidelity Low-bitrate Neural Speech Codecs Leveraging Prompt Encoders","date":"2024-04-03","arxiv_id":"2404.02702","repositories_listed":0,"syntology":null},{"url":null,"slug":"cirp-cross-item-relational-pre-training-for","title":"CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling","date":"2024-04-02","arxiv_id":"2404.01735","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-hypergraph-neural-networks-an-in","title":"A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide","date":"2024-04-01","arxiv_id":"2404.01039","repositories_listed":0,"syntology":null},{"url":null,"slug":"sugar-pre-training-3d-visual-representations","title":"SUGAR: Pre-training 3D Visual Representations for Robotics","date":"2024-04-01","arxiv_id":"2404.01491","repositories_listed":0,"syntology":null},{"url":null,"slug":"heteromile-a-multi-level-graph-representation","title":"HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs","date":"2024-03-31","arxiv_id":"2404.00816","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-framework-for-adaptive","title":"A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation","date":"2024-03-30","arxiv_id":"2404.00268","repositories_listed":0,"syntology":null},{"url":null,"slug":"dealing-with-missing-modalities-in-multimodal","title":"Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach","date":"2024-03-28","arxiv_id":"2403.19841","repositories_listed":0,"syntology":null},{"url":null,"slug":"instruction-based-hypergraph-pretraining","title":"Instruction-based Hypergraph Pretraining","date":"2024-03-28","arxiv_id":"2403.19063","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-bad-batches-enhancing-self-supervised","title":"The Bad Batches: Enhancing Self-Supervised Learning in Image Classification Through Representative Batch Curation","date":"2024-03-28","arxiv_id":"2403.19579","repositories_listed":0,"syntology":null},{"url":null,"slug":"corast-towards-foundation-model-powered","title":"CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT","date":"2024-03-27","arxiv_id":"2403.18451","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-unified-network-for-image","title":"Multi-scale Unified Network for Image Classification","date":"2024-03-27","arxiv_id":"2403.18294","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-graph-auto-encoder-based","title":"Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification","date":"2024-03-26","arxiv_id":"2403.17500","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-machine-translation-bridge-multilingual","title":"Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?","date":"2024-03-25","arxiv_id":"2403.16777","repositories_listed":0,"syntology":null},{"url":null,"slug":"chebmixer-efficient-graph-representation","title":"ChebMixer: Efficient Graph Representation Learning with MLP Mixer","date":"2024-03-25","arxiv_id":"2403.16358","repositories_listed":0,"syntology":null},{"url":null,"slug":"cmvim-contrastive-masked-vim-autoencoder-for","title":"CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classification","date":"2024-03-25","arxiv_id":"2403.16520","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-graph-representation-learning-with","title":"Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks","date":"2024-03-25","arxiv_id":"2403.17040","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-medical-image","title":"Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes","date":"2024-03-25","arxiv_id":"2403.16499","repositories_listed":0,"syntology":null},{"url":null,"slug":"akbr-learning-adaptive-kernel-based","title":"AKBR: Learning Adaptive Kernel-based Representations for Graph Classification","date":"2024-03-24","arxiv_id":"2403.16130","repositories_listed":0,"syntology":null},{"url":null,"slug":"edit3k-universal-representation-learning-for","title":"Edit3K: Universal Representation Learning for Video Editing Components","date":"2024-03-24","arxiv_id":"2403.16048","repositories_listed":0,"syntology":null},{"url":null,"slug":"pshop-a-lightweight-feed-forward-method-for","title":"PSHop: A Lightweight Feed-Forward Method for 3D Prostate Gland Segmentation","date":"2024-03-24","arxiv_id":"2403.15971","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifiable-latent-neural-causal-models","title":"Identifiable Latent Neural Causal Models","date":"2024-03-23","arxiv_id":"2403.15711","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-grounding-of-semantic-vectors-improves","title":"Brain-aligning of semantic vectors improves neural decoding of visual stimuli","date":"2024-03-22","arxiv_id":"2403.15176","repositories_listed":0,"syntology":null},{"url":null,"slug":"cell-variational-information-bottleneck","title":"Cell Variational Information Bottleneck Network","date":"2024-03-22","arxiv_id":"2403.15082","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-on-multimodal-analysis","title":"Contrastive Learning on Multimodal Analysis of Electronic Health Records","date":"2024-03-22","arxiv_id":"2403.14926","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-input-auto-encoder-guided-feature","title":"Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems","date":"2024-03-22","arxiv_id":"2403.15511","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-backbone-framework-for","title":"Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks","date":"2024-03-22","arxiv_id":"2403.15248","repositories_listed":0,"syntology":null},{"url":null,"slug":"twin-auto-encoder-model-for-learning","title":"Twin Auto-Encoder Model for Learning Separable Representation in Cyberattack Detection","date":"2024-03-22","arxiv_id":"2403.15509","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-task-unification-in-graph","title":"Exploring Task Unification in Graph Representation Learning via Generative Approach","date":"2024-03-21","arxiv_id":"2403.14340","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-decomposable-and-debiased","title":"Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks","date":"2024-03-21","arxiv_id":"2403.14140","repositories_listed":0,"syntology":null},{"url":null,"slug":"xlavs-r-cross-lingual-audio-visual-speech","title":"XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception","date":"2024-03-21","arxiv_id":"2403.14402","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-to-3d-shape-generation","title":"Text-to-3D Shape Generation","date":"2024-03-20","arxiv_id":"2403.13289","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-contrastive-learning-strategy","title":"Automated Contrastive Learning Strategy Search for Time Series","date":"2024-03-19","arxiv_id":"2403.12641","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-channel-multiplex-graph-neural-networks","title":"Dual-Channel Multiplex Graph Neural Networks for Recommendation","date":"2024-03-18","arxiv_id":"2403.11624","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-partial-label-learning-with-potential","title":"Graph Partial Label Learning with Potential Cause Discovering","date":"2024-03-18","arxiv_id":"2403.11449","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypervq-mlr-based-vector-quantization-in","title":"HyperVQ: MLR-based Vector Quantization in Hyperbolic Space","date":"2024-03-18","arxiv_id":"2403.13015","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-benefits-of-projection-head","title":"Investigating the Benefits of Projection Head for Representation Learning","date":"2024-03-18","arxiv_id":"2403.11391","repositories_listed":0,"syntology":null},{"url":null,"slug":"mlvicx-multi-level-variance-covariance","title":"MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning","date":"2024-03-18","arxiv_id":"2403.11504","repositories_listed":0,"syntology":null},{"url":null,"slug":"offline-multitask-representation-learning-for","title":"Offline Multitask Representation Learning for Reinforcement Learning","date":"2024-03-18","arxiv_id":"2403.11574","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-enhanced-representation-learning-for","title":"Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics","date":"2024-03-18","arxiv_id":"2403.11495","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-imu-based-cross-modal-transfer","title":"A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition","date":"2024-03-17","arxiv_id":"2403.15444","repositories_listed":0,"syntology":null},{"url":null,"slug":"v2x-dgw-domain-generalization-for-multi-agent","title":"V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions","date":"2024-03-17","arxiv_id":"2403.11371","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-world-modeling-with-asymmetric","title":"Probabilistic World Modeling with Asymmetric Distance Measure","date":"2024-03-16","arxiv_id":"2403.10875","repositories_listed":0,"syntology":null},{"url":null,"slug":"scheduling-drone-and-mobile-charger-via","title":"Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning","date":"2024-03-16","arxiv_id":"2403.10761","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-step-automated-cybercrime-coded-word","title":"Two-step Automated Cybercrime Coded Word Detection using Multi-level Representation Learning","date":"2024-03-16","arxiv_id":"2403.10838","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-collection-of-the-accepted-papers-for-the","title":"A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024","date":"2024-03-14","arxiv_id":"2403.10561","repositories_listed":0,"syntology":null},{"url":null,"slug":"anatomical-structure-guided-medical-vision","title":"Anatomical Structure-Guided Medical Vision-Language Pre-training","date":"2024-03-14","arxiv_id":"2403.09294","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-reusability-and-compositionality","title":"Towards the Reusability and Compositionality of Causal Representations","date":"2024-03-14","arxiv_id":"2403.09830","repositories_listed":0,"syntology":null},{"url":null,"slug":"weaksurg-weakly-supervised-surgical","title":"WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity","date":"2024-03-14","arxiv_id":"2403.09551","repositories_listed":0,"syntology":null},{"url":null,"slug":"drfer-learning-disentangled-representations","title":"DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition","date":"2024-03-13","arxiv_id":"2403.08318","repositories_listed":0,"syntology":null},{"url":null,"slug":"himap-hybrid-representation-learning-for-end","title":"HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction","date":"2024-03-13","arxiv_id":"2403.08639","repositories_listed":0,"syntology":null},{"url":null,"slug":"link-prediction-for-social-networks-using","title":"Link Prediction for Social Networks using Representation Learning and Heuristic-based Features","date":"2024-03-13","arxiv_id":"2403.08613","repositories_listed":0,"syntology":null},{"url":null,"slug":"lg-traj-llm-guided-pedestrian-trajectory","title":"LG-Traj: LLM Guided Pedestrian Trajectory Prediction","date":"2024-03-12","arxiv_id":"2403.08032","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-framework-for-deep-learning","title":"Towards a Framework for Deep Learning Certification in Safety-Critical Applications Using Inherently Safe Design and Run-Time Error Detection","date":"2024-03-12","arxiv_id":"2403.14678","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-representation-learning-game-for-classes-of","title":"A representation-learning game for classes of prediction tasks","date":"2024-03-11","arxiv_id":"2403.06971","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-what-typical-fault-signals-look","title":"Interpreting What Typical Fault Signals Look Like via Prototype-matching","date":"2024-03-11","arxiv_id":"2403.07033","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-embedding-masked-autoencoder-for-self","title":"Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain","date":"2024-03-11","arxiv_id":"2403.06432","repositories_listed":0,"syntology":null},{"url":null,"slug":"leoclr-leveraging-original-images-for","title":"LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations","date":"2024-03-11","arxiv_id":"2403.06813","repositories_listed":0,"syntology":null},{"url":null,"slug":"re-simulation-based-self-supervised-learning","title":"Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models","date":"2024-03-11","arxiv_id":"2403.07066","repositories_listed":0,"syntology":null},{"url":null,"slug":"signn-a-spike-induced-graph-neural-network","title":"SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning","date":"2024-03-11","arxiv_id":"2404.07941","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-and-mitigating-human-labelling","title":"Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning","date":"2024-03-10","arxiv_id":"2403.06289","repositories_listed":0,"syntology":null},{"url":null,"slug":"cscnet-class-specified-cascaded-network-for","title":"CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning","date":"2024-03-09","arxiv_id":"2403.05924","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-query-classification-in-e","title":"Hierarchical Query Classification in E-commerce Search","date":"2024-03-09","arxiv_id":"2403.06021","repositories_listed":0,"syntology":null},{"url":null,"slug":"advances-of-deep-learning-in-protein-science","title":"Advances of Deep Learning in Protein Science: A Comprehensive Survey","date":"2024-03-08","arxiv_id":"2403.05314","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-autoregressive-representation","title":"Denoising Autoregressive Representation Learning","date":"2024-03-08","arxiv_id":"2403.05196","repositories_listed":0,"syntology":null},{"url":null,"slug":"poly-view-contrastive-learning","title":"Poly-View Contrastive Learning","date":"2024-03-08","arxiv_id":"2403.05490","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-privileged-information-enhances","title":"Synthetic Privileged Information Enhances Medical Image Representation Learning","date":"2024-03-08","arxiv_id":"2403.05220","repositories_listed":0,"syntology":null},{"url":null,"slug":"unlocking-the-potential-of-multimodal-unified","title":"Enhancing Multimodal Unified Representations for Cross Modal Generalization","date":"2024-03-08","arxiv_id":"2403.05168","repositories_listed":0,"syntology":null},{"url":null,"slug":"control-based-graph-embeddings-with-data","title":"Control-based Graph Embeddings with Data Augmentation for Contrastive Learning","date":"2024-03-07","arxiv_id":"2403.04923","repositories_listed":0,"syntology":null},{"url":null,"slug":"medflip-medical-vision-and-language-self","title":"MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder","date":"2024-03-07","arxiv_id":"2403.04626","repositories_listed":0,"syntology":null},{"url":null,"slug":"mobius-transform-for-mitigating-perspective","title":"Möbius Transform for Mitigating Perspective Distortions in Representation Learning","date":"2024-03-07","arxiv_id":"2405.02296","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-discovering-and-merging-for","title":"Adaptive Discovering and Merging for Incremental Novel Class Discovery","date":"2024-03-06","arxiv_id":"2403.03382","repositories_listed":0,"syntology":null}],"record_sha256":"4004f69d214ec9af4ae4aabb459b7d5168e03380330dcb6ccb11a7cd10a40f27","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}