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Found 7 papers, 6 papers with code
The least-control principle for local learning at equilibrium
As special cases, they include models of great current interest in both neuroscience and machine learning, such as deep neural networks, equilibrium recurrent neural networks, deep equilibrium models, or meta-learning.
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.
Credit Assignment in Neural Networks through Deep Feedback Control
The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output.
Challenges for Using Impact Regularizers to Avoid Negative Side Effects
Finally, we explore promising directions to overcome the unsolved challenges in preventing negative side effects with impact regularizers.
Neural networks with late-phase weights
The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD).
Ranked #67 on
Image Classification
on CIFAR-100
(using extra training data)
A Theoretical Framework for Target Propagation
Here, we analyze target propagation (TP), a popular but not yet fully understood alternative to BP, from the standpoint of mathematical optimization.
Continual Learning in Recurrent Neural Networks
Here, we provide the first comprehensive evaluation of established CL methods on a variety of sequential data benchmarks.
