Papers Self-Supervised Learning
“Self-Supervised Learning” 태그가 달린 논문 5,929편 · 필터 해제
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit negative-pair construction and raw-input re…
Graph Representation LearningSelf-Supervised LearningGraph ClassificationGraph RegressionBuilding a User Foundation Model for the Open Web
User foundation models have demonstrated strong results in e-commerce and social recommendation, but most industrial deployments assume environments where user identity is stable and persistent. Open-web real-time biddin…
Self-Supervised LearningDissecting Sensitivity to Training Language in Self-Supervised Speech Learning Using Neural Audio Codec Tokens
Neural audio codecs (NACs) have become popular for obtaining speech representations as discrete tokens. Beyond compression, discrete tokens can be used to train self-supervised learning (SSL) models. Such models, referre…
Self-Supervised LearningNeonatal Hypoxic-ischaemic Encephalopathy Classification from the EEG and HRV Signals Using a Conformer based Masked Autoencoder
In this paper, we propose the MAEConformer, a novel self-supervised learning framework that combines the Conformer architecture with the Masked Autoencoder (MAE) paradigm for large-scale representation learning from unla…
Self-Supervised LearningRepresentation LearningFrom RLVR to RLSVR: Task Transformation Induces Self-Verifiable Rewards for Open-Ended LLM Self-Improvement
Reinforcement Learning with Verifiable Rewards (RLVR) has driven recent progress in reasoning-oriented large language models (LLMs) by enabling large-scale optimization. However, its applicability remains largely limited…
Self-Supervised LearningReinforcement LearningMathematical ReasoningText SummarizationA Scale-adaptive Vision Model Links C. elegans Neuronal Morphology to Behavior for Neurotoxicity Assessment
Neurological disorders are a leading cause of global disability and are increasingly linked to environmental chemical exposures. Yet neurotoxicity assessment still relies on hand-scored morphological readouts that are su…
Self-Supervised LearningDrug DiscoverySelf-Supervised Learning of Structured Dynamics from Videos
Understanding motion in video is a fundamental challenge for visual learning, as frame-to-frame change entangles two sources of dynamics: camera motion and object motion. This decomposition has remained underexplored in …
Self-Supervised LearningRepresentation LearningMultimodal Pretraining for Generalizable EEG Representation Learning
Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks. This limited approach can make it challenging to apply these models across different datasets or in various situatio…
Self-Supervised LearningRepresentation LearningSeizure DetectionSHFormer: Dynamic Spectral Filtering Convolutional Neural Network and High-pass Kernel Generation Transformer for Adaptive MRI Reconstruction
Attention Mechanism (AM) selectively focuses on essential information for imaging tasks and captures relationships between distant pixel neighborhoods to compute feature representations. Accelerated MRI reconstruction be…
Self-Supervised LearningMRI ReconstructionSentence Splitter: Uncovering Latent Factual Structure for Self-Supervised Learning
This paper introduces Sentence Splitter, a self-supervised framework built upon a T5-based encoder--decoder architecture for uncovering the latent factual structure of natural language sentences. The proposed method iden…
Knowledge Graph CompletionSelf-Supervised LearningQuestion AnsweringLATTICE: Graph Self-Supervised Learning for Multimodal Spatial Omics Integration
Spatially resolved omics studies increasingly combine transcriptomic and epigenomic assays, yet downstream analysis is often still performed using single-modality pipelines. We present LATTICE (Latent Alignment of Tissue…
Self-Supervised LearningTVT-PAPD: Pathology-Aware Prototype Distillation for Self-Supervised Whole Slide Image Classification
Self-supervised learning (SSL) has emerged as an effective paradigm for learning transferable representations from large-scale unlabeled whole slide images (WSIs). However, existing SSL methods primarily learn generic vi…
Computational EfficiencySelf-Supervised LearningRepresentation LearningImage ClassificationThe evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy
Artificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex datasets for nanoparticle characterization. Recent advances in machine learning (ML) an…
Self-Supervised LearningApplying JEPA-Style Predictive Learning to JA4-Derived Network Fingerprints
I-JEPA and V-JEPA learn by matching latent predictions to target encoder outputs rather than regenerating the original input, and this has worked well for images and video. We explore whether the same objective works for…
Self-Supervised LearningRepresentation LearningFast Rates for Semi-Supervised Learning via Data-Augmentation Graph Regularization
Self-supervised learning matches supervised accuracy from a fraction of the labels, but the labeled-sample efficiency behind this has lacked a theoretical explanation. We provide one. Data augmentation induces a similari…
Self-Supervised LearningData AugmentationFMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series
In many realistic scenarios, large volumes of time series data are generated with limited or expensive annotations. This limitation makes supervised learning methods difficult to apply and leads to the use of unsupervise…
Self-Supervised LearningDeep Clustering`Attention-Guided Cross-Temporal Clustering for Self-Supervised Video Object Segmentation
Video object segmentation (VOS) is a fundamental task in video understanding, requiring accurate delineation and consistent tracking of objects across frames. While supervised methods achieve strong performance, they rel…
Video Object SegmentationSelf-Supervised LearningSelf-Supervised Pretraining Improves Cross-Site and Cross-Scale Robustness of Point Cloud Leaf-Wood Segmentation
The accuracy of existing leaf-wood segmentation methods for tree point clouds varies across forest types and sites. Self-supervised learning (SSL) on point clouds has improved the generalization of deep learning models f…
Self-Supervised LearningPoint CloudsGeometric Collapse: When Vision Models Fail to Verify Physical Causality
Recent progress in large-scale self-supervised learning has improved dense geometric prediction, but it remains unclear whether such scaling yields inference-time physical plausibility checks. We propose Scrambled Edges,…
Self-Supervised LearningScalable Perturbation Learning for Online Self-Supervised Learning in Echo State Networks
Intelligent systems should not only solve tasks but also adapt under real-world constraints. Autonomous adaptation via self-supervised learning, sequential adaptation via online learning, and memory-efficient implementat…
Self-Supervised Learning