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)

Structured chaos shapes spike-response noise entropy in balanced neural networks. - Guillaume Lajoie; Jean-Philippe Thivierge; Eric Shea-Brown (Frontiers Comput. Neurosci., 2014)

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)

Dynamic Signal Tracking in a Simple V1 Spiking Model. - Guillaume Lajoie; Lai-Sang Young (Neural Comput., 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)

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)

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 (NeurIPS, 2019)

Dimensionality compression and expansion in Deep Neural Networks. - Stefano Recanatesi; Matthew Farrell; Madhu Advani; Timothy Moore; Guillaume Lajoie; Eric Shea-Brown (CoRR, 2019)

Low-Dimensional Dynamics of Encoding and Learning in Recurrent Neural Networks. - Stefan Horoi; Victor Geadah; Guy Wolf; Guillaume Lajoie (Canadian AI, 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 (ICML, 2020)

Untangling tradeoffs between recurrence and self-attention in artificial neural networks. - Giancarlo Kerg; Bhargav Kanuparthi; Anirudh Goyal; Kyle Goyette; Yoshua Bengio; Guillaume Lajoie (NeurIPS, 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 neural networks. - Giancarlo Kerg; Bhargav Kanuparthi; Anirudh Goyal; Kyle Goyette; Yoshua Bengio; Guillaume Lajoie (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)

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)

LEAD: Least-Action Dynamics for Min-Max Optimization. - Reyhane Askari Hemmat; Amartya Mitra; Guillaume Lajoie; Ioannis Mitliagkas (CoRR, 2020)

Implicit Regularization via Neural Feature Alignment. - Aristide Baratin; Thomas George; César Laurent; R. Devon Hjelm; Guillaume Lajoie; Pascal Vincent; Simon Lacoste-Julien (AISTATS, 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 (NER, 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 (NeurIPS Datasets and Benchmarks, 2021)

Gradient Starvation: A Learning Proclivity in Neural Networks. - Mohammad Pezeshki; Sékou-Oumar Kaba; Yoshua Bengio; Aaron C. Courville; Doina Precup; Guillaume Lajoie (NeurIPS, 2021)

Efficient and robust multi-task learning in the brain with modular task primitives. - Christian David Marton; Guillaume Lajoie; Kanaka Rajan (CoRR, 2021)

Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance. - Alexander Tong 001; Guillaume Huguet; Dennis L. Shung; Amine Natik; Manik Kuchroo; Guillaume Lajoie; Guy Wolf; Smita Krishnaswamy (CoRR, 2021)

Learning function from structure in neuromorphic networks. - Laura E. Suárez; Blake A. Richards; Guillaume Lajoie; Bratislav Misic (Nat. Mach. Intell., 2021)

Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks. - Germán Abrevaya; Guillaume Dumas; Aleksandr Y. Aravkin; Peng Zheng 002; 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)

Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance. - Alexander Tong 001; Guillaume Huguet; Dennis L. Shung; Amine Natik; Manik Kuchroo; Guillaume Lajoie; Guy Wolf; Smita Krishnaswamy (ICASSP, 2022)

Continuous-Time Meta-Learning with Forward Mode Differentiation. - Tristan Deleu; David Kanaa; Leo Feng; Giancarlo Kerg; Yoshua Bengio; Guillaume Lajoie; Pierre-Luc Bacon (ICLR, 2022)

Compositional Attention: Disentangling Search and Retrieval. - Sarthak Mittal; Sharath Chandra Raparthy; Irina Rish; Yoshua Bengio; Guillaume Lajoie (ICLR, 2022)

Multi-scale Feature Learning Dynamics: Insights for Double Descent. - Mohammad Pezeshki; Amartya Mitra; Yoshua Bengio; Guillaume Lajoie (ICML, 2022)

Exploring the Geometry and Topology of Neural Network Loss Landscapes. - Stefan Horoi; Jessie Huang; Bastian Rieck; Guillaume Lajoie; Guy Wolf; Smita Krishnaswamy (IDA, 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 (MLSP, 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 (NeurIPS, 2022)

Is a Modular Architecture Enough? - Sarthak Mittal; Yoshua Bengio; Guillaume Lajoie (NeurIPS, 2022)

Clarifying MCMC-based training of modern EBMs : Contrastive Divergence versus Maximum Likelihood. - Léo Gagnon; Guillaume Lajoie (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)

From Points to Functions: Infinite-dimensional Representations in Diffusion Models. - Sarthak Mittal; Guillaume Lajoie; Stefan Bauer; Arash Mehrjou (CoRR, 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)

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)

Performance-gated deliberation: A context-adapted strategy in which urgency is opportunity cost. - Maximilian Puelma Touzel; Paul Cisek; Guillaume Lajoie (PLoS Comput. Biol., 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)

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., 2022)

Reliability of CKA as a Similarity Measure in Deep Learning. - MohammadReza Davari; Stefan Horoi; Amine Natik; Guillaume Lajoie; Guy Wolf; Eugene Belilovsky (ICLR, 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 (ICLR, 2023)

Flexible Phase Dynamics for Bio-Plausible Contrastive Learning. - Ezekiel Williams; Colin Bredenberg; Guillaume Lajoie (ICML, 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 (NeurIPS, 2023)

Formalizing locality for normative synaptic plasticity models. - Colin Bredenberg; Ezekiel Williams; Cristina Savin; Blake A. Richards; Guillaume Lajoie (NeurIPS, 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)

Steerable Equivariant Representation Learning. - Sangnie Bhardwaj; Willie McClinton; Tongzhou Wang 001; Guillaume Lajoie; Chen Sun 002; Phillip Isola; Dilip Krishnan (CoRR, 2023)

Discrete, compositional, and symbolic representations through attractor dynamics. - Andrew Nam; Eric Elmoznino; Nikolay Malkin; Chen Sun 007; Yoshua Bengio; Guillaume Lajoie (CoRR, 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)

LEAD: Min-Max Optimization from a Physical Perspective. - Reyhane Askari Hemmat; Amartya Mitra; Guillaume Lajoie; Ioannis Mitliagkas (Trans. Mach. Learn. Res., 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)

Delta-AI: Local objectives for amortized inference in sparse graphical models. - Jean-Pierre R. Falet; Hae Beom Lee; Nikolay Malkin; Chen Sun 007; Dragos Secrieru; Dinghuai Zhang; Guillaume Lajoie; Yoshua Bengio (ICLR, 2024)

Amortizing intractable inference in large language models. - Edward J. Hu; Moksh Jain; Eric Elmoznino; Younesse Kaddar; Guillaume Lajoie; Yoshua Bengio; Nikolay Malkin (ICLR, 2024)

Sufficient conditions for offline reactivation in recurrent neural networks. - Nanda H. Krishna; Colin Bredenberg; Daniel Levenstein; Blake Aaron Richards; Guillaume Lajoie (ICLR, 2024)

Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency. - Tianhong Li; Sangnie Bhardwaj; Yonglong Tian; Han Zhang 010; Jarred Barber; Dina Katabi; Guillaume Lajoie; Huiwen Chang; Dilip Krishnan (ICLR, 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 (ICLR, 2024)

Synaptic Weight Distributions Depend on the Geometry of Plasticity. - Roman Pogodin; Jonathan Cornford; Arna Ghosh; Gauthier Gidel; Guillaume Lajoie; Blake Aaron Richards (ICLR, 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)

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)

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)

A Complexity-Based Theory of Compositionality. - Eric Elmoznino; Thomas Jiralerspong; Yoshua Bengio; Guillaume Lajoie (CoRR, 2024)

Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization. - Victor Geadah; Stefan Horoi; Giancarlo Kerg; Guy Wolf; Guillaume Lajoie (PLoS Comput. Biol., 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 (ICASSP, 2025)

Accelerating Training with Neuron Interaction and Nowcasting Networks. - Boris Knyazev 001; Abhinav Moudgil; Guillaume Lajoie; Eugene Belilovsky; Simon Lacoste-Julien (ICLR, 2025)

Multi-agent cooperation through learning-aware policy gradients. - Alexander Meulemans; Seijin Kobayashi; Johannes von Oswald; Nino Scherrer; Eric Elmoznino; Blake Aaron Richards; Guillaume Lajoie; Blaise Agüera y Arcas; João Sacramento (ICLR, 2025)

Expressivity of Neural Networks with Random Weights and Learned Biases. - Ezekiel Williams; Alexandre Payeur; Avery Hee-Woon Ryoo; Thomas Jiralerspong; Matthew G. Perich; Luca Mazzucato; Guillaume Lajoie (ICLR, 2025)

Robust prior-biased acquisition function for human-in-the-loop Bayesian optimization. - Rose Guay-Hottin; Lison Kardassevitch; Hugo Pham; Guillaume Lajoie; Marco Bonizzato (Knowl. Based Syst., 2025)

Celo: Training Versatile Learned Optimizers on a Compute Diet. - Abhinav Moudgil; Boris Knyazev 001; Guillaume Lajoie; Eugene Belilovsky (Trans. Mach. Learn. Res., 2025)

Learning Versatile Optimizers on a Compute Diet. - Abhinav Moudgil; Boris Knyazev 001; Guillaume Lajoie; Eugene Belilovsky (CoRR, 2025)

Amortized In-Context Bayesian Posterior Estimation. - Sarthak Mittal; Niels Bracher; Guillaume Lajoie; Priyank Jaini; Marcus A. Brubaker (CoRR, 2025)

In-Context Parametric Inference: Point or Distribution Estimators? - Sarthak Mittal; Yoshua Bengio; Nikolay Malkin; Guillaume Lajoie (CoRR, 2025)

Bidirectional Information Flow (BIF) - A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization. - Juan David Guerra; Thomas Garbay; Guillaume Lajoie; Marco Bonizzato (CoRR, 2025)

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training. - Johannes von Oswald; Nino Scherrer; Seijin Kobayashi; Luca Versari; Songlin Yang; Maximilian Schlegel; Kaitlin Maile; Yanick Schimpf; Oliver Sieberling; Alexander Meulemans; Rif A. Saurous; Guillaume Lajoie; et al. (CoRR, 2025)

Generalizable, real-time neural decoding with hybrid state-space models. - Avery Hee-Woon Ryoo; Nanda H. Krishna; Ximeng Mao; Mehdi Azabou; Eva L. Dyer; Matthew G. Perich; Guillaume Lajoie (CoRR, 2025)

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective. - Léo Gagnon; Eric Elmoznino; Sarthak Mittal; Tom Marty; Tejas Kasetty; Dhanya Sridhar; Guillaume Lajoie (CoRR, 2025)