Guillaume Hennequin
13 papers · 2009–2024 · 2 venues · across top CS/AI venues
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Research topics
Machine Learning
(12)
Optimization & Theory
(11)
Artificial Intelligence
(6)
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(6)
Interdisciplinary
(5)
Core Methods
(5)
Science
(5)
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(4)
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(4)
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(4)
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(3)
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Bayesian & Probabilistic Β· Machine Learning
(2)
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Keywords
second-order optimization
(4)
gaussian process
(3)
latent variable model
(3)
latent dynamics
(3)
bayesian inference
(2)
factor analysis
(2)
natural gradient
(2)
variational inference
(2)
dynamical system
(2)
control theory
(2)
gradient descent
(2)
recurrent neural network
(2)
neural data
(2)
probabilistic inference
(1)
policy gradient
(1)
feature learning
(1)
computational neuroscience
(1)
catastrophic forgetting
(1)
neural tangent kernel
(1)
convex optimization
(1)
Papers
Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization
NeurIPS 2024
Learning interpretable control inputs and dynamics underlying animal locomotion
ICLR 2024
Second-order forward-mode optimization of recurrent neural networks for neuroscience
NeurIPS 2024
Fisher-Legendre (FishLeg) optimization of deep neural networks
ICLR 2023
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data
ICLR 2022
Natural continual learning: success is a journey, not (just) a destination
NeurIPS 2021
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
NeurIPS 2021
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
NeurIPS 2020
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
NeurIPS 2020
Exact natural gradient in deep linear networks and its application to the nonlinear case
NeurIPS 2018
Analog Memories in a Balanced Rate-Based Network of E-I Neurons
NeurIPS 2014
Fast Sampling-Based Inference in Balanced Neuronal Networks
NeurIPS 2014
Code-specific policy gradient rules for spiking neurons
NeurIPS 2009