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Machine Learning

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Research Frontiers in Continual Learning and Catastrophic Forgetting

Methods enabling models to learn sequentially from new tasks without degrading performance on previously learned information.

Synaptic Plasticity Mechanisms in Continual Neural Networks
Memory Consolidation Through Dynamic Task Boundaries
Interference Geometry in Sequential Learning Landscapes
Orthogonal Feature Spaces Under Streaming Data
Rehearsal-Free Episodic Memory in Neural Systems
Prototype Drift and Identity Preservation Across Tasks
Metaplasticity: Learning to Learn Without Forgetting
Capacity-Plasticity Trade-offs in Continual Architectures
Neuron Specialization Under Non-Stationary Task Sequences
Implicit Regularization of Task-Relevant Subspaces

All Machine Learning PhD categories