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Found 4 papers, 4 papers with code
Minimizing Control for Credit Assignment with Strong Feedback
Building upon deep feedback control (DFC), a recently proposed credit assignment method, we combine strong feedback influences on neural activity with gradient-based learning and show that this naturally leads to a novel view on neural network optimization.
Uncertainty estimation under model misspecification in neural network regression
Although neural networks are powerful function approximators, the underlying modelling assumptions ultimately define the likelihood and thus the hypothesis class they are parameterizing.
Posterior Meta-Replay for Continual Learning
We offer a practical deep learning implementation of our framework based on probabilistic task-conditioned hypernetworks, an approach we term posterior meta-replay.
Continual Learning in Recurrent Neural Networks
Here, we provide the first comprehensive evaluation of established CL methods on a variety of sequential data benchmarks.
