Guillaume Lajoie

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 79

Top Topics

Modular Architecture

Opportunity Cost

Contrastive Divergence

Top Venues

Publications

Ezekiel Williams, Avery Hee-Woon Ryoo, Thomas Jiralerspong, Alexandre Payeur, Matthew G. Perich, Luca Mazzucato, Guillaume Lajoie
Expressivity of Neural Networks with Random Weights and Learned Biases. CoRR (2024)
Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford, Stefan Mihalas, Eric Shea-Brown, Guillaume Lajoie
How connectivity structure shapes rich and lazy learning in neural circuits. ICLR (2024)
Sarthak Mittal, Eric Elmoznino, Léo Gagnon, Sangnie Bhardwaj, Dhanya Sridhar, Guillaume Lajoie
Does learning the right latent variables necessarily improve in-context learning? CoRR (2024)
Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Nikolay Malkin
Amortizing intractable inference in large language models. ICLR (2024)
Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency. ICLR (2024)
Jean-Pierre Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio
Delta-AI: Local objectives for amortized inference in sparse graphical models. ICLR (2024)
Seijin Kobayashi, Simon Schug, Yassir Akram, Florian Redhardt, Johannes von Oswald, Razvan Pascanu, Guillaume Lajoie, João Sacramento
When can transformers compositionally generalize in-context? CoRR (2024)
Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake Aaron Richards
Synaptic Weight Distributions Depend on the Geometry of Plasticity. ICLR (2024)
Nanda H. Krishna, Colin Bredenberg, Daniel Levenstein, Blake Aaron Richards, Guillaume Lajoie
Sufficient conditions for offline reactivation in recurrent neural networks. ICLR (2024)
Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Nikolay Malkin
Amortizing intractable inference in large language models. CoRR (2023)
Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake A. Richards
Synaptic Weight Distributions Depend on the Geometry of Plasticity. CoRR (2023)
Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency. CoRR (2023)
Ezekiel Williams, Colin Bredenberg, Guillaume Lajoie
Flexible Phase Dynamics for Bio-Plausible Contrastive Learning. ICML (2023)
MohammadReza Davari, Stefan Horoi, Amine Natik, Guillaume Lajoie, Guy Wolf, Eugene Belilovsky
Reliability of CKA as a Similarity Measure in Deep Learning. ICLR (2023)
Colin Bredenberg, Ezekiel Williams, Cristina Savin, Blake A. Richards, Guillaume Lajoie
Formalizing locality for normative synaptic plasticity models. NeurIPS (2023)
Xu Ji, Eric Elmoznino, George Deane, Axel Constant, Guillaume Dumas, Guillaume Lajoie, Jonathan Simon, Yoshua Bengio
Sources of Richness and Ineffability for Phenomenally Conscious States. CoRR (2023)
Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao, Santosh Nachimuthu, Michael Mendelson, Blake A. Richards, Matthew G. Perich, Guillaume Lajoie, Eva L. Dyer
A Unified, Scalable Framework for Neural Population Decoding. NeurIPS (2023)
Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas
LEAD: Min-Max Optimization from a Physical Perspective. Trans. Mach. Learn. Res. 2023 (2023)
Ezekiel Williams, Colin Bredenberg, Guillaume Lajoie
Flexible Phase Dynamics for Bio-Plausible Contrastive Learning. CoRR (2023)
Arna Ghosh, Yuhan Helena Liu, Guillaume Lajoie, Konrad P. Körding, Blake Aaron Richards
How gradient estimator variance and bias impact learning in neural networks. ICLR (2023)
Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford, Stefan Mihalas, Eric Shea-Brown, Guillaume Lajoie
How connectivity structure shapes rich and lazy learning in neural circuits. CoRR (2023)
Sangnie Bhardwaj, Willie McClinton, Tongzhou Wang, Guillaume Lajoie, Chen Sun, Phillip Isola, Dilip Krishnan
Steerable Equivariant Representation Learning. CoRR (2023)
Andrew Nam, Eric Elmoznino, Nikolay Malkin, Chen Sun, Yoshua Bengio, Guillaume Lajoie
Discrete, compositional, and symbolic representations through attractor dynamics. CoRR (2023)
Jean-Pierre Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio
Delta-AI: Local objectives for amortized inference in sparse graphical models. CoRR (2023)
Damjan Kalajdzievski, Ximeng Mao, Pascal Fortier-Poisson, Guillaume Lajoie, Blake Aaron Richards
Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer. Trans. Mach. Learn. Res. 2023 (2023)
Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao, Santosh Nachimuthu, Michael J. Mendelson, Blake A. Richards, Matthew G. Perich, Guillaume Lajoie, Eva L. Dyer
A Unified, Scalable Framework for Neural Population Decoding. CoRR (2023)
Erica L. Busch, Jessie Huang, Andrew Benz, Tom Wallenstein, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy, Nicholas B. Turk-Browne
Multi-view manifold learning of human brain-state trajectories. Nat. Comput. Sci. 3 (3) (2023)
Matthew Farrell, Stefano Recanatesi, Timothy Moore, Guillaume Lajoie, Eric Shea-Brown
Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion. Nat. Mac. Intell. 4 (11) (2022)
Sarthak Mittal, Yoshua Bengio, Guillaume Lajoie
Is a Modular Architecture Enough? NeurIPS (2022)
MohammadReza Davari, Stefan Horoi, Amine Natik, Guillaume Lajoie, Guy Wolf, Eugene Belilovsky
Reliability of CKA as a Similarity Measure in Deep Learning. CoRR (2022)
Léo Gagnon, Guillaume Lajoie
Clarifying MCMC-based training of modern EBMs : Contrastive Divergence versus Maximum Likelihood. CoRR (2022)
Alexander Tong, Guillaume Huguet, Dennis L. Shung, Amine Natik, Manik Kuchroo, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy
Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance. ICASSP (2022)
Stefan Horoi, Jessie Huang, Bastian Rieck, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy
Exploring the Geometry and Topology of Neural Network Loss Landscapes. IDA (2022)
Yuhan Helena Liu, Arna Ghosh, Blake A. Richards, Eric Shea-Brown, Guillaume Lajoie
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules. CoRR (2022)
Jessie Huang, Erica L. Busch, Tom Wallenstein, Michal Gerasimiuk, Andrew Benz, Guillaume Lajoie, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy
Learning shared neural manifolds from multi-subject FMRI data. CoRR (2022)
Jessie Huang, Erica L. Busch, Tom Wallenstein, Michal Gerasimiuk, Andrew Benz, Guillaume Lajoie, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy
Learning Shared Neural Manifolds from Multi-Subject FMRI Data. MLSP (2022)
Sarthak Mittal, Guillaume Lajoie, Stefan Bauer, Arash Mehrjou
From Points to Functions: Infinite-dimensional Representations in Diffusion Models. CoRR (2022)
Yuhan Helena Liu, Arna Ghosh, Blake A. Richards, Eric Shea-Brown, Guillaume Lajoie
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules. NeurIPS (2022)
Thomas George, Guillaume Lajoie, Aristide Baratin
Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty. CoRR (2022)
Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon
Continuous-Time Meta-Learning with Forward Mode Differentiation. CoRR (2022)
Sarthak Mittal, Yoshua Bengio, Guillaume Lajoie
Is a Modular Architecture Enough? CoRR (2022)
Matthew Farrell, Stefano Recanatesi, Timothy Moore, Guillaume Lajoie, Eric Shea-Brown
Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion. Nat. Mach. Intell. 4 (6) (2022)
Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie
Compositional Attention: Disentangling Search and Retrieval. ICLR (2022)
Damjan Kalajdzievski, Ximeng Mao, Pascal Fortier-Poisson, Guillaume Lajoie, Blake A. Richards
Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer. CoRR (2022)
Maximilian Puelma Touzel, Paul Cisek, Guillaume Lajoie
Performance-gated deliberation: A context-adapted strategy in which urgency is opportunity cost. PLoS Comput. Biol. 18 (5) (2022)
Thomas George, Guillaume Lajoie, Aristide Baratin
Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty. Trans. Mach. Learn. Res. 2022 (2022)
Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie
Multi-scale Feature Learning Dynamics: Insights for Double Descent. ICML (2022)
Ryan Vogt, Maximilian Puelma Touzel, Eli Shlizerman, Guillaume Lajoie
On Lyapunov Exponents for RNNs: Understanding Information Propagation Using Dynamical Systems Tools. Frontiers Appl. Math. Stat. 8 (2022)
Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon
Continuous-Time Meta-Learning with Forward Mode Differentiation. ICLR (2022)
Giancarlo Kerg, Sarthak Mittal, David Rolnick, Yoshua Bengio, Blake A. Richards, Guillaume Lajoie
On Neural Architecture Inductive Biases for Relational Tasks. CoRR (2022)
Alexander Tong, Guillaume Huguet, Dennis L. Shung, Amine Natik, Manik Kuchroo, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy
Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance. CoRR (2021)
Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Jimenez Rezende, Michael Mozer, Yoshua Bengio, Chris Pal
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. NeurIPS Datasets and Benchmarks (2021)
Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie
Gradient Starvation: A Learning Proclivity in Neural Networks. NeurIPS (2021)
Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Christopher J. Pal
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. CoRR (2021)
Laura E. Suárez, Blake A. Richards, Guillaume Lajoie, Bratislav Misic
Learning function from structure in neuromorphic networks. Nat. Mach. Intell. 3 (9) (2021)
German Abrevaya, Guillaume Dumas, Aleksandr Y. Aravkin, Peng Zheng, Jean-Christophe Gagnon-Audet, James R. Kozloski, Pablo Polosecki, Guillaume Lajoie, David D. Cox, Silvina Ponce Dawson, Guillermo A. Cecchi, Irina Rish
Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks. Neural Comput. 33 (8) (2021)
Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie
Compositional Attention: Disentangling Search and Retrieval. CoRR (2021)
Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie
Multi-scale Feature Learning Dynamics: Insights for Double Descent. CoRR (2021)
Christian David Marton, Guillaume Lajoie, Kanaka Rajan
Efficient and robust multi-task learning in the brain with modular task primitives. CoRR (2021)
Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien
Implicit Regularization via Neural Feature Alignment. AISTATS (2021)
Olivier Tessier-Larivière, Luke Y. Prince, Pascal Fortier-Poisson, Lorenz Wernisch, Oliver Armitage, Emil Hewage, Guillaume Lajoie, Blake A. Richards
PNS-GAN: Conditional Generation of Peripheral Nerve Signals in the Wavelet Domain via Adversarial Networks. NER (2021)
Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien
Implicit Regularization in Deep Learning: A View from Function Space. CoRR (2020)
Ryan Vogt, Maximilian Puelma Touzel, Eli Shlizerman, Guillaume Lajoie
On Lyapunov Exponents for RNNs: Understanding Information Propagation Using Dynamical Systems Tools. CoRR (2020)
Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. ICML (2020)
Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie
Untangling tradeoffs between recurrence and self-attention in neural networks. CoRR (2020)
Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas
LEAD: Least-Action Dynamics for Min-Max Optimization. CoRR (2020)
Stefan Horoi, Guillaume Lajoie, Guy Wolf
Internal representation dynamics and geometry in recurrent neural networks. CoRR (2020)
Stefan Horoi, Victor Geadah, Guy Wolf, Guillaume Lajoie
Low-Dimensional Dynamics of Encoding and Learning in Recurrent Neural Networks. Canadian Conference on AI (2020)
Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie
Gradient Starvation: A Learning Proclivity in Neural Networks. CoRR (2020)
Victor Geadah, Giancarlo Kerg, Stefan Horoi, Guy Wolf, Guillaume Lajoie
Advantages of biologically-inspired adaptive neural activation in RNNs during learning. CoRR (2020)
Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. CoRR (2020)
Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie
Untangling tradeoffs between recurrence and self-attention in artificial neural networks. NeurIPS (2020)
Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio, Guillaume Lajoie
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics. CoRR (2019)
Stefano Recanatesi, Matthew Farrell, Madhu Advani, Timothy Moore, Guillaume Lajoie, Eric Shea-Brown
Dimensionality compression and expansion in Deep Neural Networks. CoRR (2019)
Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio, Guillaume Lajoie
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics. NeurIPS (2019)
Guillaume Lajoie, Nedialko I. Krouchev, John F. Kalaska, Adrienne L. Fairhall, Eberhard E. Fetz
Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface. PLoS Comput. Biol. 13 (2) (2017)
Guillaume Lajoie, Kevin K. Lin, Jean-Philippe Thivierge, Eric Shea-Brown
Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems. PLoS Comput. Biol. 12 (12) (2016)
Philippe Vincent-Lamarre, Guillaume Lajoie, Jean-Philippe Thivierge
Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks. J. Comput. Neurosci. 41 (3) (2016)
Guillaume Lajoie, Lai-Sang Young
Dynamic Signal Tracking in a Simple V1 Spiking Model. Neural Comput. 28 (9) (2016)
Guillaume Lajoie, Jean-Philippe Thivierge, Eric Shea-Brown
Structured chaos shapes spike-response noise entropy in balanced neural networks. Frontiers Comput. Neurosci. 8 (2014)
Guillaume Lajoie, Eric Shea-Brown
Shared Inputs, Entrainment, and Desynchrony in Elliptic Bursters: From Slow Passage to Discontinuous Circle Maps. SIAM J. Appl. Dyn. Syst. 10 (4) (2011)