Guillaume Lajoie

According to our database, Guillaume Lajoie authored at least 65 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Multi-agent cooperation through learning-aware policy gradients.

Alexander Meulemans, Seijin Kobayashi, Johannes von Oswald, Nino Scherrer, Eric Elmoznino, Blake A. Richards, Guillaume Lajoie, Blaise Agüera y Arcas, João Sacramento

CoRR, 2024

A Complexity-Based Theory of Compositionality.

Eric Elmoznino, Thomas Jiralerspong, Yoshua Bengio, Guillaume Lajoie

CoRR, 2024

In-context learning and Occam's razor.

Eric Elmoznino, Tom Marty, Tejas Kasetty, Léo Gagnon, Sarthak Mittal, Mahan Fathi, Dhanya Sridhar, Guillaume Lajoie

CoRR, 2024

Latent Representation Learning for Multimodal Brain Activity Translation.

Arman Afrasiyabi, Dhananjay Bhaskar, Erica L. Busch, Laurent Caplette, Rahul Singh, Guillaume Lajoie, Nicholas B. Turk-Browne, Smita Krishnaswamy

CoRR, 2024

Accelerating Training with Neuron Interaction and Nowcasting Networks.

Boris Knyazev, Abhinav Moudgil, Guillaume Lajoie, Eugene Belilovsky, Simon Lacoste-Julien

CoRR, 2024

When can transformers compositionally generalize in-context?

Seijin Kobayashi, Simon Schug, Yassir Akram, Florian Redhardt, Johannes von Oswald, Razvan Pascanu, Guillaume Lajoie, João Sacramento

CoRR, 2024

Expressivity of Neural Networks with Random Weights and Learned Biases.

Ezekiel Williams, Avery Hee-Woon Ryoo, Thomas Jiralerspong, Alexandre Payeur, Matthew G. Perich, Luca Mazzucato, Guillaume Lajoie

CoRR, 2024

Does learning the right latent variables necessarily improve in-context learning?

Sarthak Mittal, Eric Elmoznino, Léo Gagnon, Sangnie Bhardwaj, Dhanya Sridhar, Guillaume Lajoie

CoRR, 2024

Synaptic Weight Distributions Depend on the Geometry of Plasticity.

Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake Aaron Richards

Proceedings of the Twelfth International Conference on Learning Representations, 2024

How connectivity structure shapes rich and lazy learning in neural circuits.

Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford, Stefan Mihalas, Eric Shea-Brown, Guillaume Lajoie

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency.

Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Sufficient conditions for offline reactivation in recurrent neural networks.

Nanda H. Krishna, Colin Bredenberg, Daniel Levenstein, Blake Aaron Richards, Guillaume Lajoie

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Amortizing intractable inference in large language models.

Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Nikolay Malkin

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Delta-AI: Local objectives for amortized inference in sparse graphical models.

Jean-Pierre R. Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer.

Damjan Kalajdzievski, Ximeng Mao, Pascal Fortier-Poisson, Guillaume Lajoie, Blake Aaron Richards

Trans. Mach. Learn. Res., 2023

LEAD: Min-Max Optimization from a Physical Perspective.

Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas

Trans. Mach. Learn. Res., 2023

Multi-view manifold learning of human brain-state trajectories.

Erica L. Busch, Jessie Huang, Andrew Benz, Tom Wallenstein, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy, Nicholas B. Turk-Browne

Nat. Comput. Sci., 2023

Discrete, compositional, and symbolic representations through attractor dynamics.

Andrew Nam, Eric Elmoznino, Nikolay Malkin, Chen Sun, Yoshua Bengio, Guillaume Lajoie

CoRR, 2023

Steerable Equivariant Representation Learning.

Sangnie Bhardwaj, Willie McClinton, Tongzhou Wang, Guillaume Lajoie, Chen Sun, Phillip Isola, Dilip Krishnan

CoRR, 2023

Sources of Richness and Ineffability for Phenomenally Conscious States.

Xu Ji, Eric Elmoznino, George Deane, Axel Constant, Guillaume Dumas, Guillaume Lajoie, Jonathan Simon, Yoshua Bengio

CoRR, 2023

Formalizing locality for normative synaptic plasticity models.

Colin Bredenberg, Ezekiel Williams, Cristina Savin, Blake A. Richards, Guillaume Lajoie

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Unified, Scalable Framework for Neural Population Decoding.

Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao, Santosh Nachimuthu, Michael Mendelson, Blake A. Richards, Matthew G. Perich, Guillaume Lajoie, Eva L. Dyer

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Flexible Phase Dynamics for Bio-Plausible Contrastive Learning.

Ezekiel Williams, Colin Bredenberg, Guillaume Lajoie

Proceedings of the International Conference on Machine Learning, 2023

How gradient estimator variance and bias impact learning in neural networks.

Arna Ghosh, Yuhan Helena Liu, Guillaume Lajoie, Konrad P. Körding, Blake Aaron Richards

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Reliability of CKA as a Similarity Measure in Deep Learning.

MohammadReza Davari, Stefan Horoi, Amine Natik, Guillaume Lajoie, Guy Wolf, Eugene Belilovsky

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion.

Matthew Farrell, Stefano Recanatesi, Timothy Moore, Guillaume Lajoie, Eric Shea-Brown

Nat. Mac. Intell., November, 2022

Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty.

Thomas George, Guillaume Lajoie, Aristide Baratin

Trans. Mach. Learn. Res., 2022

Performance-gated deliberation: A context-adapted strategy in which urgency is opportunity cost.

Maximilian Puelma Touzel, Paul Cisek, Guillaume Lajoie

PLoS Comput. Biol., 2022

Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion.

Matthew Farrell, Stefano Recanatesi, Timothy Moore, Guillaume Lajoie, Eric Shea-Brown

Nat. Mach. Intell., 2022

On Lyapunov Exponents for RNNs: Understanding Information Propagation Using Dynamical Systems Tools.

Ryan Vogt, Maximilian Puelma Touzel, Eli Shlizerman, Guillaume Lajoie

Frontiers Appl. Math. Stat., 2022

From Points to Functions: Infinite-dimensional Representations in Diffusion Models.

Sarthak Mittal, Guillaume Lajoie, Stefan Bauer, Arash Mehrjou

CoRR, 2022

On Neural Architecture Inductive Biases for Relational Tasks.

Giancarlo Kerg, Sarthak Mittal, David Rolnick, Yoshua Bengio, Blake A. Richards, Guillaume Lajoie

CoRR, 2022

Clarifying MCMC-based training of modern EBMs : Contrastive Divergence versus Maximum Likelihood.

Léo Gagnon, Guillaume Lajoie

CoRR, 2022

Is a Modular Architecture Enough?

Sarthak Mittal, Yoshua Bengio, Guillaume Lajoie

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules.

Yuhan Helena Liu, Arna Ghosh, Blake A. Richards, Eric Shea-Brown, Guillaume Lajoie

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Shared Neural Manifolds from Multi-Subject FMRI Data.

Jessie Huang, Erica L. Busch, Tom Wallenstein, Michal Gerasimiuk, Andrew Benz, Guillaume Lajoie, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy

Proceedings of the 32nd IEEE International Workshop on Machine Learning for Signal Processing, 2022

Exploring the Geometry and Topology of Neural Network Loss Landscapes.

Stefan Horoi, Jessie Huang, Bastian Rieck, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy

Proceedings of the Advances in Intelligent Data Analysis XX, 2022

Multi-scale Feature Learning Dynamics: Insights for Double Descent.

Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie

Proceedings of the International Conference on Machine Learning, 2022

Compositional Attention: Disentangling Search and Retrieval.

Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie

Proceedings of the Tenth International Conference on Learning Representations, 2022

Continuous-Time Meta-Learning with Forward Mode Differentiation.

Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon

Proceedings of the Tenth International Conference on Learning Representations, 2022

Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance.

Alexander Tong, Guillaume Huguet, Dennis L. Shung, Amine Natik, Manik Kuchroo, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks.

Germán 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

Neural Comput., 2021

Learning function from structure in neuromorphic networks.

Laura E. Suárez, Blake A. Richards, Guillaume Lajoie, Bratislav Misic

Nat. Mach. Intell., 2021

Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance.

Alexander Tong, Guillaume Huguet, Dennis L. Shung, Amine Natik, Manik Kuchroo, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy

CoRR, 2021

Efficient and robust multi-task learning in the brain with modular task primitives.

Christian David Marton, Guillaume Lajoie, Kanaka Rajan

CoRR, 2021

Gradient Starvation: A Learning Proclivity in Neural Networks.

Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning.

Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Jimenez Rezende, Michael Mozer, Yoshua Bengio, Chris Pal

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

PNS-GAN: Conditional Generation of Peripheral Nerve Signals in the Wavelet Domain via Adversarial Networks.

Olivier Tessier-Larivière, Luke Y. Prince, Pascal Fortier-Poisson, Lorenz Wernisch, Oliver Armitage, Emil Hewage, Guillaume Lajoie, Blake A. Richards

Proceedings of the 10th International IEEE/EMBS Conference on Neural Engineering, 2021

Implicit Regularization via Neural Feature Alignment.

Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

LEAD: Least-Action Dynamics for Min-Max Optimization.

Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas

CoRR, 2020

Implicit Regularization in Deep Learning: A View from Function Space.

Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien

CoRR, 2020

Advantages of biologically-inspired adaptive neural activation in RNNs during learning.

Victor Geadah, Giancarlo Kerg, Stefan Horoi, Guy Wolf, Guillaume Lajoie

CoRR, 2020

Untangling tradeoffs between recurrence and self-attention in neural networks.

Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie

CoRR, 2020

Internal representation dynamics and geometry in recurrent neural networks.

Stefan Horoi, Guillaume Lajoie, Guy Wolf

CoRR, 2020

Untangling tradeoffs between recurrence and self-attention in artificial neural networks.

Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules.

Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio

Proceedings of the 37th International Conference on Machine Learning, 2020

Low-Dimensional Dynamics of Encoding and Learning in Recurrent Neural Networks.

Stefan Horoi, Victor Geadah, Guy Wolf, Guillaume Lajoie

Proceedings of the Advances in Artificial Intelligence, 2020

2019

Dimensionality compression and expansion in Deep Neural Networks.

Stefano Recanatesi, Matthew Farrell, Madhu Advani, Timothy Moore, Guillaume Lajoie, Eric Shea-Brown

CoRR, 2019

Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics.

Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio, Guillaume Lajoie

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.

Guillaume Lajoie, Nedialko I. Krouchev, John F. Kalaska, Adrienne L. Fairhall, Eberhard E. Fetz

PLoS Comput. Biol., 2017

2016

Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems.

Guillaume Lajoie, Kevin K. Lin, Jean-Philippe Thivierge, Eric Shea-Brown

PLoS Comput. Biol., 2016

Dynamic Signal Tracking in a Simple V1 Spiking Model.

Guillaume Lajoie, Lai-Sang Young

Neural Comput., 2016

Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks.

Philippe Vincent-Lamarre, Guillaume Lajoie, Jean-Philippe Thivierge

J. Comput. Neurosci., 2016

2014

Structured chaos shapes spike-response noise entropy in balanced neural networks.

Guillaume Lajoie, Jean-Philippe Thivierge, Eric Shea-Brown

Frontiers Comput. Neurosci., 2014

2011

Shared Inputs, Entrainment, and Desynchrony in Elliptic Bursters: From Slow Passage to Discontinuous Circle Maps.

Guillaume Lajoie, Eric Shea-Brown

SIAM J. Appl. Dyn. Syst., 2011