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ORCIDGuillaume Hennequin
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2020 – today
- 2024
[c12]Thomas Soares Mullen, Marine Schimel, Guillaume Hennequin, Christian K. Machens, Michael B. Orger, Adrien Jouary
:
Learning interpretable control inputs and dynamics underlying animal locomotion. ICLR 2024- 2023
- [c11]Jezabel R. Garcia, Federica Freddi, Stathi Fotiadis, Maolin Li, Sattar Vakili, Alberto Bernacchia, Guillaume Hennequin:
Fisher-Legendre (FishLeg) optimization of deep neural networks. ICLR 2023 - 2022
- [c10]Marine Schimel, Ta-Chu Kao, Kristopher T. Jensen, Guillaume Hennequin:
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data. ICLR 2022 - 2021
- [c9]Kristopher T. Jensen, Ta-Chu Kao, Jasmine Stone, Guillaume Hennequin:
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models. NeurIPS 2021: 10613-10626 - [c8]Ta-Chu Kao, Kristopher T. Jensen, Gido van de Ven, Alberto Bernacchia, Guillaume Hennequin:
Natural continual learning: success is a journey, not (just) a destination. NeurIPS 2021: 28067-28079 - [i4]Ta-Chu Kao, Kristopher T. Jensen, Alberto Bernacchia, Guillaume Hennequin:
Natural continual learning: success is a journey, not (just) a destination. CoRR abs/2106.08085 (2021) - 2020
- [c7]Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin:
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. NeurIPS 2020 - [c6]Virginia Rutten, Alberto Bernacchia, Maneesh Sahani, Guillaume Hennequin:
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data. NeurIPS 2020 - [i3]Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin:
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. CoRR abs/2006.07429 (2020) - [i2]Ta-Chu Kao, Guillaume Hennequin:
Automatic differentiation of Sylvester, Lyapunov, and algebraic Riccati equations. CoRR abs/2011.11430 (2020)
2010 – 2019
- 2019
- [i1]Giacomo Baggio, Virginia Rutten, Guillaume Hennequin, Sandro Zampieri:
Efficient Communication over Complex Dynamical Networks. CoRR abs/1904.02447 (2019) - 2018
- [c5]Giacomo Baggio, Virginia Rutten, Guillaume Hennequin, Sandro Zampieri:
Information Transmission in Dynamical Networks: The Normal Network Case. CDC 2018: 2543-2548 - [c4]Alberto Bernacchia, Máté Lengyel, Guillaume Hennequin:
Exact natural gradient in deep linear networks and its application to the nonlinear case. NeurIPS 2018: 5945-5954 - 2014
- [c3]Dylan Festa, Guillaume Hennequin, Máté Lengyel:
Analog Memories in a Balanced Rate-Based Network of E-I Neurons. NIPS 2014: 2231-2239 - [c2]Guillaume Hennequin, Laurence Aitchison, Máté Lengyel:
Fast Sampling-Based Inference in Balanced Neuronal Networks. NIPS 2014: 2240-2248 - 2013
- [j2]Friedemann Zenke, Guillaume Hennequin, Wulfram Gerstner:
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector. PLoS Comput. Biol. 9(11) (2013) - 2010
- [j1]Guillaume Hennequin, Wulfram Gerstner, Jean-Pascal Pfister:
STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission. Frontiers Comput. Neurosci. 4: 143 (2010)
2000 – 2009
- 2009
- [c1]Henning Sprekeler, Guillaume Hennequin, Wulfram Gerstner:
Code-specific policy gradient rules for spiking neurons. NIPS 2009: 1741-1749
Coauthor Index
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