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Papers

Showing 1–28 of 28 papers

TitleStatusHype
Improving and Understanding Variational Continual LearningCode1
Learning Invariant Representation for Continual LearningCode1
Self-Attention Meta-Learner for Continual LearningCode1
Automating Continual LearningCode1
Task-conditioned Ensemble of Expert Models for Continuous LearningCode0
SpaceNet: Make Free Space For Continual LearningCode0
Negotiated Representations to Prevent Forgetting in Machine Learning ApplicationsCode0
On Sequential Loss Approximation for Continual LearningCode0
Mixture-of-Variational-Experts for Continual LearningCode0
Discriminative Variational Autoencoder for Continual Learning with Generative Replay—0
Elephant Neural Networks: Born to Be a Continual Learner—0
Enabling Continual Learning with Differentiable Hebbian Plasticity—0
Hard ASH: Sparsity and the right optimizer make a continual learner—0
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures—0
Neuromodulated Neural Architectures with Local Error Signals for Memory-Constrained Online Continual Learning—0
Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning—0
Reducing catastrophic forgetting with learning on synthetic data—0
Task-agnostic Continual Learning with Hybrid Probabilistic Models—0
Active Dendrites Enable Efficient Continual Learning in Time-To-First-Spike Neural Networks—0
Towards Robust Continual Learning with Bayesian Adaptive Moment Regularization—0
A Neural Network Model of Complementary Learning Systems: Pattern Separation and Completion for Continual Learning—0
Attention-Based Structural-Plasticity—0
Bio-Inspired, Task-Free Continual Learning through Activity Regularization—0
Conditional Input Gated Low-Rank Perturbations for Continual Learning—0
Continual Competitive Memory: A Neural System for Online Task-Free Lifelong Learning—0
Dendritic Self-Organizing Maps for Continual Learning—0
Differentiable Hebbian Consolidation for Continual Learning—0
Differentiable Hebbian Plasticity for Continual Learning—0
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