arXiv AI
ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids
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arXiv AI
Jul 21
ProDER: A Continual Learning Approach for Fault Prediction in Evolving Smart Grids
arXiv:2511. 05420v2 Announce Type: replace-cross Abstract: As smart grids evolve to meet growing energy demands and modern operational challenges, the ability to accurately predict faults becomes increasingly critical.
arXiv AI
Oct 26
RobustNCL: Robustifying Neuromorphic Continual Learning under Transient Faults with Lightweight Detect-and-Cure Strategy
RobustNCL is a lightweight framework that enhances the reliability of neuromorphic continual learning (NCL) systems against transient hardware faults. It detects faults during weight access from off‑chip memory and cures affected weights using one of three strategies—zero replacement, min/max value, or most‑probable value—storing curing values in radiation‑hardened memory. Experiments on Split MNIST, Split Fashion‑MNIST, Split CIFAR‑10, and Split CIFAR‑100 show that RobustNCL maintains accuracy within 0.5% of fault‑free models and significantly outperforms baseline NCL methods while adding negligible latency and energy overhead.
arXiv Machine Learning
Aug 3
TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners
arXiv:2607. 29592v1 Announce Type: cross Abstract: The primary challenge of continual learning (CL) systems is to learn new tasks while remaining performant on previously learned tasks.
arXiv AI
Jun 16
When Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learning
arXiv:2606. 15695v1 Announce Type: cross Abstract: Federated class-incremental learning (FCIL) becomes substantially harder when clients observe different label subsets, progress through tasks at different stages, and provide uneven supervision for the same semantic concepts.
arXiv AI
Jul 21
The Art of Not Forgetting
arXiv:2607. 17944v1 Announce Type: cross Abstract: We introduce CMP (Cognitive Memory Primitive), an architecture that represents inputs as sparse relational codes, stores them in a two-tier competitive memory, and learns entirely through local, gradient-free updates, with no backpropagation anywhere in the network.
arXiv Machine Learning
Oct 27
CARES: Coverage-Aware Replay Exemplar Selection for Continual Learning
arXiv:2610.32799v1 Announce Type: new Abstract: Replay-based Continual Learning mitigates catastrophic forgetting by keeping a buffer of past samples, making buffer composition critical under a fixed...