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Guillaume Lajoie
Guillaume Lajoie
Assistant Professor, Applied Mathematics, Université de Montréal
Verified email at umontreal.ca - Homepage
Title
Cited by
Cited by
Year
Chaos and reliability in balanced spiking networks with temporal drive
G Lajoie, KK Lin, E Shea-Brown
Physical Review E 87 (5), 052901, 2013
462013
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, 2021
412021
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
412019
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
312019
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
302020
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 (3), 305-322, 2016
202016
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
192014
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
162016
Learning function from structure in neuromorphic networks
LE Suárez, BA Richards, G Lajoie, B Misic
Nature Machine Intelligence 3 (9), 771-786, 2021
152021
Shared inputs, entrainment, and desynchrony in elliptic bursters: From slow passage to discontinuous circle maps
G Lajoie, E Shea-Brown
SIAM Journal on Applied Dynamical Systems 10 (4), 1232-1271, 2011
142011
On lyapunov exponents for rnns: Understanding information propagation using dynamical systems tools
R Vogt, MP Touzel, E Shlizerman, G Lajoie
arXiv preprint arXiv:2006.14123, 2020
132020
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
132017
Dynamic compression and expansion in a classifying recurrent network
MS Farrell, S Recanatesi, G Lajoie, E Shea-Brown
bioRxiv, 564476, 2019
112019
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
112019
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), 1-13, 2021
92021
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
82021
Recurrent neural networks learn robust representations by dynamically balancing compression and expansion. bioRxiv
M Farrell, S Recanatesi, T Moore, G Lajoie, E Shea-Brown
December 3, 564476, 2019
82019
Lead: Least-action dynamics for min-max optimization
RA Hemmat, A Mitra, G Lajoie, I Mitliagkas
arXiv preprint arXiv:2010.13846, 2020
62020
Cortical network mechanisms of anodal and cathodal transcranial direct current stimulation in awake primates
AR Bogaard, G Lajoie, H Boyd, A Morse, S Zanos, EE Fetz
bioRxiv, 516260, 2019
62019
Predictive learning extracts latent space representations from sensory observations
S Recanatesi, M Farrell, G Lajoie, S Deneve, M Rigotti, E Shea-Brown
bioRxiv, 2019
52019
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