1 code implementation • 18 Feb 2024 • Benjamin Scellier
In the quest for energy-efficient artificial intelligence systems, resistor networks are attracting interest as an alternative to conventional GPU-based neural networks.
1 code implementation • NeurIPS 2023 • Benjamin Scellier, Maxence Ernoult, Jack Kendall, Suhas Kumar
Additionally, we establish new SOTA results with DCHNs on all five datasets, both in performance and speed.
no code implementations • 22 Dec 2023 • Benjamin Scellier, Siddhartha Mishra
Resistor networks have recently had a surge of interest as substrates for energy-efficient self-learning machines.
no code implementations • 30 May 2022 • Benjamin Scellier, Siddhartha Mishra, Yoshua Bengio, Yann Ollivier
This work establishes that a physical system can perform statistical learning without gradient computations, via an Agnostic Equilibrium Propagation (Aeqprop) procedure that combines energy minimization, homeostatic control, and nudging towards the correct response.
no code implementations • 18 Mar 2021 • Benjamin Scellier
Traditionally in deep learning, neural networks are differentiable mathematical functions, and the loss gradients required for SGD are computed with the backpropagation algorithm.
no code implementations • 14 Jan 2021 • Axel Laborieux, Maxence Ernoult, Benjamin Scellier, Yoshua Bengio, Julie Grollier, Damien Querlioz
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.
1 code implementation • 6 Jun 2020 • Axel Laborieux, Maxence Ernoult, Benjamin Scellier, Yoshua Bengio, Julie Grollier, Damien Querlioz
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.
no code implementations • 2 Jun 2020 • Jack Kendall, Ross Pantone, Kalpana Manickavasagam, Yoshua Bengio, Benjamin Scellier
We introduce a principled method to train end-to-end analog neural networks by stochastic gradient descent.
no code implementations • 29 Apr 2020 • Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier
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.
no code implementations • 29 Apr 2020 • Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier
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.
2 code implementations • NeurIPS 2019 • Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier
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.
3 code implementations • 14 Aug 2018 • Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio
The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists.
no code implementations • ICLR 2018 • Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio
The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists.
1 code implementation • 22 Nov 2017 • Benjamin Scellier, Yoshua Bengio
Recurrent Backpropagation and Equilibrium Propagation are supervised learning algorithms for fixed point recurrent neural networks which differ in their second phase.
no code implementations • 6 Jun 2016 • Yoshua Bengio, Benjamin Scellier, Olexa Bilaniuk, Joao Sacramento, Walter Senn
We find conditions under which a simple feedforward computation is a very good initialization for inference, after the input units are clamped to observed values.
2 code implementations • 16 Feb 2016 • Benjamin Scellier, Yoshua Bengio
Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point, or stationary distribution) towards a configuration that reduces prediction error.