Papers Self-Learning
“Self-Learning” 태그가 달린 논문 404편 · 필터 해제
Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision
Contrastive learning (CL) has become a cornerstone of self-supervised pretraining (SSP) in foundation models, however, extending CL to pixel-wise representation, crucial for medical vision, remains an open problem. Stand…
Contrastive LearningFeature CorrelationregressionSelf-LearningReal Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory
This paper presents a novel method for enhancing the adaptability of Proportional-Integral-Derivative (PID) controllers in industrial systems using event-based dynamic game theory, which enables the PID controllers to se…
Boundary DetectionSelf-LearningSelf-learning signal classifier for decameter coherent scatter radars
The paper presents a method for automatic constructing a classifier for processed data obtained by decameter coherent scatter radars. Method is based only on the radar data obtained, the results of automatic modeling of …
Self-LearningLearning long range dependencies through time reversal symmetry breaking
Deep State Space Models (SSMs) reignite physics-grounded compute paradigms, as RNNs could natively be embodied into dynamical systems. This calls for dedicated learning algorithms obeying to core physical principles, wit…
Self-LearningState Space ModelsConfidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving
Autonomous driving promises significant advancements in mobility, road safety and traffic efficiency, yet reinforcement learning and imitation learning face safe-exploration and distribution-shift challenges. Although hu…
Autonomous DrivingImitation LearningSafe ExplorationSelf-LearningChain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects
The rapid evolution of large language models in natural language processing has substantially elevated their semantic understanding and logical reasoning capabilities. Such proficiencies have been leveraged in autonomous…
Autonomous DrivingLogical ReasoningSelf-LearningDiverse, not Short: A Length-Controlled Self-Learning Framework for Improving Response Diversity of Language Models
Diverse language model responses are crucial for creative generation, open-ended tasks, and self-improvement training. We show that common diversity metrics, and even reward models used for preference optimization, syste…
DiversitySelf-LearningSelf-Learning Hyperspectral and Multispectral Image Fusion via Adaptive Residual Guided Subspace Diffusion Model
Hyperspectral and multispectral image (HSI-MSI) fusion involves combining a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to generate a high-resolution hyperspectral imag…
Computational EfficiencySelf-LearningTowards a Deeper Understanding of Reasoning Capabilities in Large Language Models
While large language models demonstrate impressive performance on static benchmarks, the true potential of large language models as self-learning and reasoning agents in dynamic environments remains unclear. This study s…
Large Language ModelMathSelf-LearningChannel-Adaptive Robust Resource Allocation for Highly Reliable IRS-Assisted V2X Communications
This paper addresses the challenges of resource allocation in vehicular networks enhanced by Intelligent Reflecting Surfaces (IRS), considering the uncertain Channel State Information (CSI) typical of vehicular environme…
Self-LearningSecure Estimation of Battery Voltage Under Sensor Attacks: A Self-Learning Koopman Approach
Cloud-based battery management system (BMS) requires accurate terminal voltage measurement data to ensure optimal and safe charging of Lithium-ion batteries. Unfortunately, an adversary can corrupt the battery terminal v…
Self-LearningSpatial-Geometry Enhanced 3D Dynamic Snake Convolutional Neural Network for Hyperspectral Image Classification
Deep neural networks face several challenges in hyperspectral image classification, including complex and sparse ground object distributions, small clustered structures, and elongated multi-branch features that often lea…
Hyperspectral Image Classificationimage-classificationImage ClassificationSelf-LearningThe Self-Learning Agent with a Progressive Neural Network Integrated Transformer
This paper introduces a self-learning agent that integrates LLaMA 3.2 with a Progressive Neural Network (PNN) for continual learning in conversational AI and code generation. The framework dynamically collects data, fine…
Code GenerationContinual LearningMeta-LearningSelf-LearningHarnessing uncertainty when learning through Equilibrium Propagation in neural networks
Equilibrium Propagation (EP) is a supervised learning algorithm that trains network parameters using local neuronal activity. This is in stark contrast to backpropagation, where updating the parameters of the network req…
Self-LearningAutomated and Risk-Aware Engine Control Calibration Using Constrained Bayesian Optimization
Decarbonization of the transport sector sets increasingly strict demands to maximize thermal efficiency and minimize greenhouse gas emissions of Internal Combustion Engines. This has led to complex engines with a surge i…
Bayesian OptimizationSelf-LearningWill Pre-Training Ever End? A First Step Toward Next-Generation Foundation MLLMs via Self-Improving Systematic Cognition
Recent progress in (multimodal) large language models ((M)LLMs) has shifted focus from pre-training to inference-time compute scaling and post-training optimization, driven by concerns over limited high-quality real-worl…
Caption GenerationImage CaptioningMultimodal ReasoningSelf-LearningSTART: Self-taught Reasoner with Tools
Large reasoning models (LRMs) like OpenAI-o1 and DeepSeek-R1 have demonstrated remarkable capabilities in complex reasoning tasks through the utilization of long Chain-of-thought (CoT). However, these models often suffer…
MathSelf-LearningLearning Conjecturing from Scratch
We develop a self-learning approach for conjecturing of induction predicates on a dataset of 16197 problems derived from the OEIS. These problems are hard for today's SMT and ATP systems because they require a combinatio…
Self-LearningA Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition
Zero-shot named entity recognition (NER) aims to develop entity recognition systems from unannotated text corpora. This task presents substantial challenges due to minimal human intervention. Recent work has adapted larg…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+3Requirements for Quality Assurance of AI Models for Early Detection of Lung Cancer
Lung cancer is the second most common cancer and the leading cause of cancer-related deaths worldwide. Survival largely depends on tumor stage at diagnosis, and early detection with low-dose CT can significantly reduce m…
Self-LearningSpecificity