Guillaume Hennequin

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 13

Top Topics

Gaussian Processes

Latent Structure

Neuronal Networks

Content Addressable

Top Venues

Publications

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)
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)
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)
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)
Ta-Chu Kao, Kristopher T. Jensen, Alberto Bernacchia, Guillaume Hennequin
Natural continual learning: success is a journey, not (just) a destination. CoRR (2021)
Kristopher T. Jensen, Ta-Chu Kao, Jasmine Stone, Guillaume Hennequin
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models. NeurIPS (2021)
Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. NeurIPS (2020)
Ta-Chu Kao, Guillaume Hennequin
Automatic differentiation of Sylvester, Lyapunov, and algebraic Riccati equations. CoRR (2020)
Virginia Rutten, Alberto Bernacchia, Maneesh Sahani, Guillaume Hennequin
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data. NeurIPS (2020)
Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. CoRR (2020)
Giacomo Baggio, Virginia Rutten, Guillaume Hennequin, Sandro Zampieri
Efficient Communication over Complex Dynamical Networks. CoRR (2019)
Giacomo Baggio, Virginia Rutten, Guillaume Hennequin, Sandro Zampieri
Information Transmission in Dynamical Networks: The Normal Network Case. CDC (2018)
Alberto Bernacchia, Máté Lengyel, Guillaume Hennequin
Exact natural gradient in deep linear networks and its application to the nonlinear case. NeurIPS (2018)
Guillaume Hennequin, Laurence Aitchison, Máté Lengyel
Fast Sampling-Based Inference in Balanced Neuronal Networks. NIPS (2014)
Dylan Festa, Guillaume Hennequin, Máté Lengyel
Analog Memories in a Balanced Rate-Based Network of E-I Neurons. NIPS (2014)
Friedemann Zenke, Guillaume Hennequin, Wulfram Gerstner
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector. PLoS Comput. Biol. 9 (11) (2013)
Guillaume Hennequin, Wulfram Gerstner, Jean-Pascal Pfister
STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission. Frontiers Comput. Neurosci. 4 (2010)
Henning Sprekeler, Guillaume Hennequin, Wulfram Gerstner
Code-specific policy gradient rules for spiking neurons. NIPS (2009)