Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible. - Yoshua Bengio; Benjamin Scellier; Olexa Bilaniuk; João Sacramento; Walter Senn (CoRR, 2016)

Towards a Biologically Plausible Backprop. - Benjamin Scellier; Yoshua Bengio (CoRR, 2016)

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation. - Benjamin Scellier; Yoshua Bengio (Frontiers Comput. Neurosci., 2017)

Extending the Framework of Equilibrium Propagation to General Dynamics. - Benjamin Scellier; Anirudh Goyal; Jonathan Binas; Thomas Mesnard; Yoshua Bengio (ICLR (Workshop), 2018)

Generalization of Equilibrium Propagation to Vector Field Dynamics. - Benjamin Scellier; Anirudh Goyal; Jonathan Binas; Thomas Mesnard; Yoshua Bengio (CoRR, 2018)

Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input. - Maxence Ernoult; Julie Grollier; Damien Querlioz; Yoshua Bengio; Benjamin Scellier (NeurIPS, 2019)

Equivalence of Equilibrium Propagation and Recurrent Backpropagation. - Benjamin Scellier; Yoshua Bengio (Neural Comput., 2019)

Equilibrium Propagation with Continual Weight Updates. - Maxence Ernoult; Julie Grollier; Damien Querlioz; Yoshua Bengio; Benjamin Scellier (CoRR, 2020)

Continual Weight Updates and Convolutional Architectures for Equilibrium Propagation. - Maxence Ernoult; Julie Grollier; Damien Querlioz; Yoshua Bengio; Benjamin Scellier (CoRR, 2020)

Training End-to-End Analog Neural Networks with Equilibrium Propagation. - Jack D. Kendall; Ross D. Pantone; Kalpana Manickavasagam; Yoshua Bengio; Benjamin Scellier (CoRR, 2020)

Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias. - Axel Laborieux; Maxence Ernoult; Benjamin Scellier; Yoshua Bengio; Julie Grollier; Damien Querlioz (CoRR, 2020)

A deep learning theory for neural networks grounded in physics. - Benjamin Scellier (CoRR, 2021)

Learning by non-interfering feedback chemical signaling in physical networks. - Vidyesh Rao Anisetti; Benjamin Scellier; J. M. Schwarz (CoRR, 2022)

Agnostic Physics-Driven Deep Learning. - Benjamin Scellier; Siddhartha Mishra; Yoshua Bengio; Yann Ollivier (CoRR, 2022)

Frequency propagation: Multi-mechanism learning in nonlinear physical networks. - Vidyesh Rao Anisetti; Ananth Kandala; Benjamin Scellier; J. M. Schwarz (CoRR, 2022)

Energy-based learning algorithms for analog computing: a comparative study. - Benjamin Scellier; Maxence Ernoult; Jack D. Kendall; Suhas Kumar (NeurIPS, 2023)

A universal approximation theorem for nonlinear resistive networks. - Benjamin Scellier; Siddhartha Mishra (CoRR, 2023)

A fast algorithm to simulate nonlinear resistive networks. - Benjamin Scellier (ICML, 2024)

Quantum Equilibrium Propagation: Gradient-Descent Training of Quantum Systems. - Benjamin Scellier (CoRR, 2024)

Training of Physical Neural Networks. - Ali Momeni; Babak Rahmani; Benjamin Scellier; Logan G. Wright; Peter L. McMahon; Clara C. Wanjura; Yuhang Li; Anas Skalli; Natalia G. Berloff; Tatsuhiro Onodera; Ilker Oguz; Francesco Morichetti; et al. (CoRR, 2024)

Frequency Propagation: Multimechanism Learning in Nonlinear Physical Networks. - Vidyesh Rao Anisetti; Ananth Kandala; Benjamin Scellier; J. M. Schwarz (Neural Comput., 2024)