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ORCIDBenjamin Scellier
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2020 – today
- 2024
[j3]Vidyesh Rao Anisetti, Ananth Kandala, Benjamin Scellier, J. M. Schwarz:
Frequency Propagation: Multimechanism Learning in Nonlinear Physical Networks. Neural Comput. 36(4): 596-620 (2024)- [c4]Benjamin Scellier:
A fast algorithm to simulate nonlinear resistive networks. ICML 2024 - [i19]Benjamin Scellier:
A Fast Algorithm to Simulate Nonlinear Resistive Networks. CoRR abs/2402.11674 (2024) - [i18]Benjamin Scellier:
Quantum Equilibrium Propagation: Gradient-Descent Training of Quantum Systems. CoRR abs/2406.00879 (2024) - [i17]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, Philipp del Hougne, Manuel Le Gallo, Abu Sebastian, Azalia Mirhoseini, Cheng Zhang, Danijela Markovic, Daniel Brunner, Christophe Moser, Sylvain Gigan, Florian Marquardt, Aydogan Ozcan, Julie Grollier, Andrea J. Liu, Demetri Psaltis, Andrea Alù, Romain Fleury:
Training of Physical Neural Networks. CoRR abs/2406.03372 (2024) - 2023
- [c3]Benjamin Scellier, Maxence Ernoult, Jack D. Kendall, Suhas Kumar:
Energy-based learning algorithms for analog computing: a comparative study. NeurIPS 2023 - [i16]Benjamin Scellier, Siddhartha Mishra:
A universal approximation theorem for nonlinear resistive networks. CoRR abs/2312.15063 (2023) - [i15]Benjamin Scellier, Maxence Ernoult, Jack D. Kendall, Suhas Kumar:
Energy-based learning algorithms for analog computing: a comparative study. CoRR abs/2312.15103 (2023) - 2022
- [i14]Vidyesh Rao Anisetti, Benjamin Scellier, J. M. Schwarz:
Learning by non-interfering feedback chemical signaling in physical networks. CoRR abs/2203.12098 (2022) - [i13]Benjamin Scellier, Siddhartha Mishra, Yoshua Bengio, Yann Ollivier:
Agnostic Physics-Driven Deep Learning. CoRR abs/2205.15021 (2022) - [i12]Vidyesh Rao Anisetti, Ananth Kandala, Benjamin Scellier, J. M. Schwarz:
Frequency propagation: Multi-mechanism learning in nonlinear physical networks. CoRR abs/2208.08862 (2022) - 2021
- [i11]Axel Laborieux, Maxence Ernoult, Benjamin Scellier, Yoshua Bengio, Julie Grollier, Damien Querlioz:
Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias. CoRR abs/2101.05536 (2021) - [i10]Benjamin Scellier:
A deep learning theory for neural networks grounded in physics. CoRR abs/2103.09985 (2021) - 2020
- [i9]Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier:
Equilibrium Propagation with Continual Weight Updates. CoRR abs/2005.04168 (2020) - [i8]Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier:
Continual Weight Updates and Convolutional Architectures for Equilibrium Propagation. CoRR abs/2005.04169 (2020) - [i7]Jack D. Kendall, Ross D. Pantone, Kalpana Manickavasagam, Yoshua Bengio, Benjamin Scellier:
Training End-to-End Analog Neural Networks with Equilibrium Propagation. CoRR abs/2006.01981 (2020) - [i6]Axel Laborieux, Maxence Ernoult, Benjamin Scellier, Yoshua Bengio, Julie Grollier, Damien Querlioz:
Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias. CoRR abs/2006.03824 (2020)
2010 – 2019
- 2019
- [j2]Benjamin Scellier, Yoshua Bengio:
Equivalence of Equilibrium Propagation and Recurrent Backpropagation. Neural Comput. 31(2) (2019) - [c2]Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier:
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input. NeurIPS 2019: 7079-7089 - [i5]Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier:
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input. CoRR abs/1905.13633 (2019) - 2018
- [c1]Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio:
Extending the Framework of Equilibrium Propagation to General Dynamics. ICLR (Workshop) 2018 - [i4]Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio:
Generalization of Equilibrium Propagation to Vector Field Dynamics. CoRR abs/1808.04873 (2018) - 2017
- [j1]Benjamin Scellier, Yoshua Bengio:
Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation. Frontiers Comput. Neurosci. 11: 24 (2017) - [i3]Benjamin Scellier, Yoshua Bengio:
Equivalence of Equilibrium Propagation and Recurrent Backpropagation. CoRR abs/1711.08416 (2017) - 2016
- [i2]Benjamin Scellier, Yoshua Bengio:
Towards a Biologically Plausible Backprop. CoRR abs/1602.05179 (2016) - [i1]Yoshua Bengio, Benjamin Scellier, Olexa Bilaniuk, João Sacramento, Walter Senn:
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible. CoRR abs/1606.01651 (2016)
Coauthor Index
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