#Continual Learning
Latest news articles tagged with "Continual Learning". Follow the timeline of events, related topics, and entities.
Articles (19)
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🇺🇸 Elastic Weight Consolidation Done Right for Continual Learning
[USA]
arXiv:2603.18596v1 Announce Type: cross Abstract: Weight regularization methods in continual learning (CL) alleviate catastrophic forgetting by assessing and penalizing changes to important model wei...
Related: #Machine Learning -
🇺🇸 Continual Multimodal Egocentric Activity Recognition via Modality-Aware Novel Detection
[USA]
arXiv:2603.16970v1 Announce Type: cross Abstract: Multimodal egocentric activity recognition integrates visual and inertial cues for robust first-person behavior understanding. However, deploying suc...
Related: #Activity Recognition -
🇺🇸 CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments
[USA]
arXiv:2603.17548v1 Announce Type: cross Abstract: Artificial intelligence systems predominantly rely on static data distributions, making them ineffective in dynamic real-world environments, such as ...
Related: #AI Adaptation -
🇺🇸 MedCL-Bench: Benchmarking stability-efficiency trade-offs and scaling in biomedical continual learning
[USA]
arXiv:2603.16738v1 Announce Type: new Abstract: Medical language models must be updated as evidence and terminology evolve, yet sequential updating can trigger catastrophic forgetting. Although biome...
Related: #Biomedical AI -
🇺🇸 Information-Theoretic Constraints for Continual Vision-Language-Action Alignment
[USA]
arXiv:2603.13335v1 Announce Type: cross Abstract: When deployed in open-ended robotic environments, Vision--Language--Action (VLA) models need to continually acquire new skills, yet suffer from sever...
Related: #Multimodal AI -
🇺🇸 Key-Value Pair-Free Continual Learner via Task-Specific Prompt-Prototype
[USA]
arXiv:2601.04864v2 Announce Type: replace Abstract: Continual learning aims to enable models to acquire new knowledge while retaining previously learned information. Prompt-based methods have shown r...
Related: #AI Adaptation -
🇺🇸 UniPrompt-CL: Sustainable Continual Learning in Medical AI with Unified Prompt Pools
[USA]
arXiv:2508.10954v2 Announce Type: replace-cross Abstract: Modern AI models are typically trained on static datasets, limiting their ability to continuously adapt to rapidly evolving real-world enviro...
Related: #Medical AI -
🇺🇸 Residual SODAP: Residual Self-Organizing Domain-Adaptive Prompting with Structural Knowledge Preservation for Continual Learning
[USA]
arXiv:2603.12816v1 Announce Type: cross Abstract: Continual learning (CL) suffers from catastrophic forgetting, which is exacerbated in domain-incremental learning (DIL) where task identifiers are un...
Related: #AI Adaptation -
🇺🇸 Representation Finetuning for Continual Learning
[USA]
arXiv:2603.11201v1 Announce Type: cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams. While pre-trained models have s...
Related: #AI Adaptation -
🇺🇸 XSkill: Continual Learning from Experience and Skills in Multimodal Agents
[USA]
arXiv:2603.12056v1 Announce Type: new Abstract: Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestrati...
Related: #Multimodal Agents -
🇺🇸 Gated Adaptation for Continual Learning in Human Activity Recognition
[USA]
arXiv:2603.10046v1 Announce Type: cross Abstract: Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart h...
Related: #Activity Recognition -
🇺🇸 LCA: Local Classifier Alignment for Continual Learning
[USA]
arXiv:2603.09888v1 Announce Type: new Abstract: A fundamental requirement for intelligent systems is the ability to learn continuously under changing environments. However, models trained in this reg...
Related: #Machine Learning -
🇺🇸 Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary
[USA]
arXiv:2603.06813v1 Announce Type: new Abstract: Reusable decision structure survives across episodes in reinforcement learning, but this depends on how the agent--world boundary is drawn. In stationa...
Related: #Multi-Agent Systems -
🇺🇸 Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning
[USA]
arXiv:2603.07972v1 Announce Type: new Abstract: While scaling individual Large Language Models (LLMs) has delivered remarkable progress, the next frontier lies in scaling collaboration through multi-...
Related: #AI Collaboration -
🇺🇸 SegReg: Latent Space Regularization for Improved Medical Image Segmentation
[USA]
arXiv:2602.23509v1 Announce Type: cross Abstract: Medical image segmentation models are typically optimised with voxel-wise losses that constrain predictions only in the output space. This leaves lat...
Related: #Medical Imaging, #Deep Learning, #U‑Net, #Latent Space Regularisation -
🇺🇸 Continual uncertainty learning
[USA]
arXiv:2602.17174v1 Announce Type: cross Abstract: Robust control of mechanical systems with multiple uncertainties remains a fundamental challenge, particularly when nonlinear dynamics and operating-...
Related: #Machine Learning, #Robust Control, #Deep Reinforcement Learning, #Simulation‑to‑Real Transfer -
🇺🇸 Continual learning and refinement of causal models through dynamic predicate invention
[USA]
arXiv:2602.17217v1 Announce Type: new Abstract: Efficiently navigating complex environments requires agents to internalize the underlying logic of their world, yet standard world modelling methods of...
Related: #Artificial Intelligence, #Causal Modeling, #Symbolic Reasoning, #Dynamic Predicate Invention -
🇺🇸 Forget Forgetting: Continual Learning in a World of Abundant Memory
[USA]
arXiv:2502.07274v5 Announce Type: replace-cross Abstract: Continual learning (CL) has traditionally focused on minimizing exemplar memory, a constraint often misaligned with modern systems where GPU ...
Related: #Memory Constraints, #GPU Time Bottleneck, #Paradigm Shift in ML Research, #Practical System Design -
🇺🇸 Task-Agnostic Continual Learning for Chest Radiograph Classification
[USA]
arXiv:2602.15811v1 Announce Type: cross Abstract: Clinical deployment of chest radiograph classifiers requires models that can be updated as new datasets become available without retraining on previo...
Related: #Artificial Intelligence in Healthcare, #Medical Imaging, #Chest X‑ray Diagnosis, #Model Deployment and Updating
Key Entities (3)
- LCA (1 news)
- Machine learning (1 news)
- EWC (1 news)
About the topic: Continual Learning
The topic "Continual Learning" aggregates 19+ news articles from various countries.