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Training End-to-End Analog Neural Networks with Equilibrium Propagation
Jack Kendall, Ross Pantone, Kalpana Manickavasagam +2
We introduce a principled method to train end-to-end analog neural networks by stochastic gradient descent. In these analog neural networks, the weights to be adjusted are implemen…
Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias
Axel Laborieux, Maxence Ernoult, Benjamin Scellier +3
Equilibrium Propagation (EP) is a biologically-inspired algorithm for convergent RNNs with a local learning rule that comes with strong theoretical guarantees. The parameter update…
Continual Weight Updates and Convolutional Architectures for Equilibrium Propagation
Maxence Ernoult, Julie Grollier, Damien Querlioz +2
Equilibrium Propagation (EP) is a biologically inspired alternative algorithm to backpropagation (BP) for training neural networks. It applies to RNNs fed by a static input x that…
Equilibrium Propagation with Continual Weight Updates
Maxence Ernoult, Julie Grollier, Damien Querlioz +2
Equilibrium Propagation (EP) is a learning algorithm that bridges Machine Learning and Neuroscience, by computing gradients closely matching those of Backpropagation Through Time (…
Attention Based Pruning for Shift Networks
Ghouthi Boukli Hacene, Carlos Lassance, Vincent Gripon +2
In many application domains such as computer vision, Convolutional Layers (CLs) are key to the accuracy of deep learning methods. However, it is often required to assemble a large…
Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies
Sarath Chandar, Chinnadhurai Sankar, Eugene Vorontsov +2
Modelling long-term dependencies is a challenge for recurrent neural networks. This is primarily due to the fact that gradients vanish during training, as the sequence length incre…