Guillaume Lajoie
Guillaume Lajoie
Assistant Professor, Applied Mathematics, Université de Montréal
Verified email at - Homepage
Cited by
Cited by
Gradient starvation: A learning proclivity in neural networks
M Pezeshki, O Kaba, Y Bengio, AC Courville, D Precup, G Lajoie
Advances in Neural Information Processing Systems 34, 1256-1272, 2021
Dimensionality compression and expansion in deep neural networks
S Recanatesi, M Farrell, M Advani, T Moore, G Lajoie, E Shea-Brown
arXiv preprint arXiv:1906.00443, 2019
Learning function from structure in neuromorphic networks
LE Suárez, BA Richards, G Lajoie, B Misic
Nature Machine Intelligence 3 (9), 771-786, 2021
Learning to combine top-down and bottom-up signals in recurrent neural networks with attention over modules
S Mittal, A Lamb, A Goyal, V Voleti, M Shanahan, G Lajoie, M Mozer, ...
International Conference on Machine Learning, 6972-6986, 2020
Non-normal recurrent neural network (nnrnn): learning long time dependencies while improving expressivity with transient dynamics
G Kerg, K Goyette, M Puelma Touzel, G Gidel, E Vorontsov, Y Bengio, ...
Advances in neural information processing systems 32, 2019
Chaos and reliability in balanced spiking networks with temporal drive
G Lajoie, KK Lin, E Shea-Brown
Physical Review E 87 (5), 052901, 2013
Predictive learning as a network mechanism for extracting low-dimensional latent space representations
S Recanatesi, M Farrell, G Lajoie, S Deneve, M Rigotti, E Shea-Brown
Nature communications 12 (1), 1417, 2021
Implicit regularization via neural feature alignment
A Baratin, T George, C Laurent, RD Hjelm, G Lajoie, P Vincent, ...
International Conference on Artificial Intelligence and Statistics, 2269-2277, 2021
Recurrent neural networks learn robust representations by dynamically balancing compression and expansion
M Farrell, S Recanatesi, T Moore, G Lajoie, E Shea-Brown
BioRxiv, 564476, 2019
On lyapunov exponents for rnns: Understanding information propagation using dynamical systems tools
R Vogt, M Puelma Touzel, E Shlizerman, G Lajoie
Frontiers in Applied Mathematics and Statistics 8, 818799, 2022
Systematic evaluation of causal discovery in visual model based reinforcement learning
NR Ke, A Didolkar, S Mittal, A Goyal, G Lajoie, S Bauer, D Rezende, ...
arXiv preprint arXiv:2107.00848, 2021
Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks
P Vincent-Lamarre, G Lajoie, JP Thivierge
Journal of computational neuroscience 41, 305-322, 2016
Untangling tradeoffs between recurrence and self-attention in artificial neural networks
G Kerg, B Kanuparthi, AG ALIAS PARTH GOYAL, K Goyette, Y Bengio, ...
Advances in Neural Information Processing Systems 33, 19443-19454, 2020
Structured chaos shapes spike-response noise entropy in balanced neural networks
G Lajoie, JP Thivierge, E Shea-Brown
Frontiers in computational neuroscience 8, 123, 2014
Is a modular architecture enough?
S Mittal, Y Bengio, G Lajoie
Advances in Neural Information Processing Systems 35, 28747-28760, 2022
Encoding in balanced networks: Revisiting spike patterns and chaos in stimulus-driven systems
G Lajoie, KK Lin, JP Thivierge, E Shea-Brown
PLoS computational biology 12 (12), e1005258, 2016
Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion
M Farrell, S Recanatesi, T Moore, G Lajoie, E Shea-Brown
Nature Machine Intelligence 4 (6), 564-573, 2022
Continuous-time meta-learning with forward mode differentiation
T Deleu, D Kanaa, L Feng, G Kerg, Y Bengio, G Lajoie, PL Bacon
arXiv preprint arXiv:2203.01443, 2022
Compositional attention: Disentangling search and retrieval
S Mittal, SC Raparthy, I Rish, Y Bengio, G Lajoie
arXiv preprint arXiv:2110.09419, 2021
Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface
G Lajoie, NI Krouchev, JF Kalaska, AL Fairhall, EE Fetz
PLoS computational biology 13 (2), e1005343, 2017
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