Search Results for author:
Found 5 papers, 2 papers with code
Unsupervised End-to-End Training with a Self-Defined Bio-Inspired Target
Current unsupervised learning methods depend on end-to-end training via deep learning techniques such as self-supervised learning, with high computational requirements, or employ layer-by-layer training using bio-inspired approaches like Hebbian learning, using local learning rules incompatible with supervised learning.
Quantum-noise-limited optical neural networks operating at a few quanta per activation
We experimentally demonstrated MNIST classification with a test accuracy of 98% using an optical neural network with a hidden layer operating in the single-photon regime; the optical energy used to perform the classification corresponds to 0. 008 photons per multiply-accumulate (MAC) operation, which is equivalent to 0. 003 attojoules of optical energy per MAC.
Training an Ising Machine with Equilibrium Propagation
Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins of Artificial Intelligence (AI).
Training Dynamical Binary Neural Networks with Equilibrium Propagation
We first train systems with binary weights and full-precision activations, achieving an accuracy equivalent to that of full-precision models trained by standard EP on MNIST, and losing only 1. 9% accuracy on CIFAR-10 with equal architecture.
EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementations
Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge.
