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Found 12 papers, 5 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.
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).
Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses
Extracting information from radiofrequency (RF) signals using artificial neural networks at low energy cost is a critical need for a wide range of applications from radars to health.
Quantum materials for energy-efficient neuromorphic computing
Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data.
Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations
Here we show that a dynamical neural network, trained on a minimal amount of data, can predict the behavior of spintronic devices with high accuracy and an extremely efficient simulation time, compared to the micromagnetic simulations that are usually employed to model them.
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.
Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias
Equilibrium Propagation (EP) is a biologically-inspired counterpart of Backpropagation Through Time (BPTT) which, owing to its strong theoretical guarantees and the locality in space of its learning rule, fosters the design of energy-efficient hardware dedicated to learning.
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.
Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias
In this work, we show that a bias in the gradient estimate of EP, inherent in the use of finite nudging, is responsible for this phenomenon and that cancelling it allows training deep ConvNets by EP.
Equilibrium Propagation with Continual Weight Updates
However, in existing implementations of EP, the learning rule is not local in time: the weight update is performed after the dynamics of the second phase have converged and requires information of the first phase that is no longer available physically.
Continual Weight Updates and Convolutional Architectures for Equilibrium Propagation
On the other hand, the biological plausibility of EP is limited by the fact that its learning rule is not local in time: the synapse update is performed after the dynamics of the second phase have converged and requires information of the first phase that is no longer available physically.
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input
Equilibrium Propagation (EP) is a biologically inspired learning algorithm for convergent recurrent neural networks, i. e. RNNs that are fed by a static input x and settle to a steady state.
