Papers Continual Learning
“Continual Learning” 태그가 달린 논문 3,520편 · 필터 해제
MePo++: Unifying Representation Refinement and Reconciliation for General Continual Learning
General continual learning (GCL) aims to learn from evolving data streams without task identities, explicit boundaries, or repeated access to previous data, making it a realistic yet challenging setting for continual int…
Continual LearningEfficient Online Continual Foundation Model Fine-Tuning for Predictive Process Monitoring
Predictive Process Monitoring (PPM) models are increasingly deployed in dynamic environments where concept drift causes the underlying process distribution to shift over time. While recent work has moved toward online co…
Continual LearningThomson: Continual Learning of Frontier Models for SovereignAI
The development of frontier models is commonly perceived to be the exclusive remit of a small number of heavily funded players, creating an information, economic and power asymmetry between developers and the diverse use…
Continual LearningPrompt EngineeringDomain AdaptationUnifying Detection and Adaptation in Task-Free Continual Learning
To mitigate catastrophic forgetting in downstream continual learning (CL) for large language models (LLMs), existing methods typically constrain parameter updates or introduce task-specific adaptation modules. However, t…
Continual LearningGeo-LoRA: Geometry-Aware Subspace Evolution for Low-Rank Adaptation in Continual Learning
Rehearsal-free class-incremental learning (CIL) with LoRA adapters remains challenging because the low-rank subspaces updated across tasks evolve without geometric control, causing unstable shared representations and rep…
class-incremental learningContinual LearningParameter Efficient Continual Learning for Sparse Event-Based Transformers
Robotic and edge intelligence systems operate in dynamic environments where data arrives continuously, requiring models to adapt while preserving previously learned knowledge under strict memory and energy constraints. W…
parameter-efficient fine-tuningclass-incremental learningContinual LearningEvent-based visionFast Weight Attention for Continual Learning
Recurrent fast-weight memories and selective state-space models compress an expanding context into a fixed-size recurrent state, making the state transition an online learning rule. We study this rule under read-after-wr…
Continual LearningContinually learning neural-operator surrogate for three-dimensional airborne electromagnetic Bayesian inversion
Three-dimensional probabilistic inversion of time-domain airborne electromagnetic (AEM) data is limited by the cost of the forward solve. Even though one simulation takes only tens of seconds, a Bayesian inversion of a s…
Continual LearningAdvantage-Driven Explicit Memory for Social Navigation
Robot policies are predominantly learned with classical parametric variants of imitation learning or RL, where training stores the agent's behavior exclusively in the policy's network parameters, putting a heavy burden o…
Representation LearningContinual LearningDecision MakingAdapter-Based Few-Shot Continual Learning for Malicious Packet Recognition
The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on new data often leads to catastrophic forge…
Few-Shot Class-Incremental LearningSelf-Supervised LearningMalware ClassificationContinual LearningReward-Free Continual Adaptation for Resilient Space Robots
Space robots operate in extreme environments where hardware degradation can critically compromise traditional control strategies. While continual reinforcement learning offers a promising mechanism for online adaptation,…
Reinforcement LearningContinual LearningSPARCL: Spectral Partitioned Analytic Continual Learning
Analytic continual learning has emerged as a strong exemplar-free alternative to gradient-based class-incremental learning because it replaces iterative optimization with closed-form ridge updates. Yet the usual forgetti…
class-incremental learningContinual LearningSocialized Division and Collaboration: Rethinking Class-Incremental Learning under Optimization Conflicts
Class-incremental learning is commonly instantiated as a single-model paradigm, where a unified model sequentially adapts to an unbounded stream of sessions. While effective under mild distributional shifts, this formula…
class-incremental learningContinual LearningAn Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage
Artificial intelligence can support cultural heritage and digital humanities through large-scale retrieval and analysis of digitized collections. However, cultural heritage data are often distributed across institutions,…
Genre classificationContinual LearningTowards general embodied intelligence: integrating large language models, knowledge bases, and reasoning capabilities to build the next generation of AI agents
The convergence of large language models (LLMs), structured knowledge bases (KBs), and reasoning ability (RA) presents a promising trajectory toward general embodied intelligence (GEI). This paper reviews the evolution o…
Continual LearningLogical ReasoningFrequency-Aware Continual Learning for Smart Contract Vulnerability Detection with Large Language Models
Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges. First, new vulnerability categories demand parameter-efficient adaptation, since full retraining is prohibi…
Vulnerability DetectionContinual LearningIn Two Minds about Lifelong Learning: Exploring Hemispheric Redundancy and Specialisation in Neural Models
Persistent intelligent systems require the ability to learn continually, but current machine learning approaches face significant challenges in this area compared to biological learning systems. Machine learning algorith…
Incremental LearningContinual LearningHarness Continual Learning: Continual Adaptation Beyond Model Parameters
Continual learning has largely been model-centric, treating model parameters as the state that changes with sequential experience. Modern agents can also adapt through a harness of prompts, memories, tools, skills, and r…
Continual LearningForgetting, plasticity, and co-observation: a third facet of continual learning
Efficient continual learning remains a fundamental challenge for deep neural networks. While catastrophic forgetting and loss of plasticity are widely considered the primary obstacles to overcome, we show that these two …
Continual LearningWhen to Review: Spaced Repetition for Continual Pre-Training of Language Models
Continual pre-training of large language models must acquire new information without erasing old knowledge. Existing replay methods often choose a global old/new mixture and sample uniformly, ignoring that examples diffe…
Continual Learning