Lajoie, Guillaume

50 publications

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

TMLR 2025 Celo: Training Versatile Learned Optimizers on a Compute Diet Abhinav Moudgil, Boris Knyazev, Guillaume Lajoie, Eugene Belilovsky

ICML 2025 Does Learning the Right Latent Variables Necessarily Improve In-Context Learning? Sarthak Mittal, Eric Elmoznino, Leo Gagnon, Sangnie Bhardwaj, Guillaume Lajoie, Dhanya Sridhar

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

NeurIPS 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

ICML 2025 In-Context Learning and Occam’s Razor Eric Elmoznino, Tom Marty, Tejas Kasetty, Leo Gagnon, Sarthak Mittal, Mahan Fathi, Dhanya Sridhar, Guillaume Lajoie

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 Aguera y Arcas, Joao Sacramento

ICML 2025 Towards a Formal Theory of Representational Compositionality Eric Elmoznino, Thomas Jiralerspong, Yoshua Bengio, Guillaume Lajoie

NeurIPS 2025 Tracing the Representation Geometry of Language Models from Pretraining to Post-Training Melody Zixuan Li, Kumar Krishna Agrawal, Arna Ghosh, Komal Kumar Teru, Adam Santoro, Guillaume Lajoie, Blake Aaron Richards

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 Delta-AI: Local Objectives for Amortized Inference in Sparse Graphical Models Jean-Pierre René Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio

ICLRW 2024 Explicit Knowledge Factorization Meets In-Context Learning: What Do We Gain? Sarthak Mittal, Eric Elmoznino, Leo Gagnon, Sangnie Bhardwaj, Dhanya Sridhar, Guillaume Lajoie

ICMLW 2024 Expressivity of Neural Networks with Fixed Weights and Learned Biases Ezekiel Williams, Avery Hee-Woon Ryoo, Thomas Jiralerspong, Alexandre Payeur, Matthew G Perich, Luca Mazzucato, Guillaume Lajoie

ICLR 2024 How Connectivity Structure Shapes Rich and Lazy Learning in Neural Circuits Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford, Stefan Mihalas, Eric Todd SheaBrown, Guillaume Lajoie

NeurIPSW 2024 Learning Stochastic Rainbow Networks Vivian White, Muawiz Sajjad Chaudhary, Guy Wolf, Guillaume Lajoie, Kameron Decker Harris

ICLRW 2024 Learning and Aligning Structured Random Feature Networks Vivian White, Muawiz Sajjad Chaudhary, Guy Wolf, Guillaume Lajoie, Kameron Decker Harris

ICLR 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

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 Synaptic Weight Distributions Depend on the Geometry of Plasticity Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake Aaron Richards

NeurIPS 2023 A Unified, Scalable Framework for Neural Population Decoding Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao, Santosh Nachimuthu, Michael Mendelson, Blake Richards, Matthew Perich, Guillaume Lajoie, Eva Dyer

NeurIPSW 2023 Discrete, Compositional, and Symbolic Representations Through Attractor Dynamics Andrew Joohun Nam, Eric Elmoznino, Nikolay Malkin, Chen Sun, Yoshua Bengio, Guillaume Lajoie

ICMLW 2023 Exploring Exchangeable Dataset Amortization for Bayesian Posterior Inference Sarthak Mittal, Niels Leif Bracher, Guillaume Lajoie, Priyank Jaini, Marcus A Brubaker

ICML 2023 Flexible Phase Dynamics for Bio-Plausible Contrastive Learning Ezekiel Williams, Colin Bredenberg, Guillaume Lajoie

NeurIPS 2023 Formalizing Locality for Normative Synaptic Plasticity Models Colin Bredenberg, Ezekiel Williams, Cristina Savin, Blake Richards, Guillaume Lajoie

ICLR 2023 How Gradient Estimator Variance and Bias Impact Learning in Neural Networks Arna Ghosh, Yuhan Helena Liu, Guillaume Lajoie, Konrad Kording, Blake Aaron Richards

TMLR 2023 LEAD: Min-Max Optimization from a Physical Perspective Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas

ICMLW 2023 LEAD: Min-Max Optimization from a Physical Perspective Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas

ICMLW 2023 Learning to Optimize with Recurrent Hierarchical Transformers Abhinav Moudgil, Boris Knyazev, Guillaume Lajoie, Eugene Belilovsky

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

NeurIPSW 2023 Sufficient Conditions for Offline Reactivation in Recurrent Neural Networks Nanda H Krishna, Colin Bredenberg, Daniel Levenstein, Blake Aaron Richards, Guillaume Lajoie

TMLR 2023 Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer Damjan Kalajdzievski, Ximeng Mao, Pascal Fortier-Poisson, Guillaume Lajoie, Blake Aaron Richards

NeurIPS 2022 Beyond Accuracy: Generalization Properties of Bio-Plausible Temporal Credit Assignment Rules Yuhan Helena Liu, Arna Ghosh, Blake Richards, Eric Shea-Brown, Guillaume Lajoie

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

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

NeurIPSW 2022 Deceiving the CKA Similarity Measure in Deep Learning MohammadReza Davari, Stefan Horoi, Amine Natik, Guillaume Lajoie, Guy Wolf, Eugene Belilovsky

ICLRW 2022 From Points to Functions: Infinite-Dimensional Representations in Diffusion Models Sarthak Mittal, Guillaume Lajoie, Stefan Bauer, Arash Mehrjou

ICLRW 2022 Inductive Biases for Relational Tasks Giancarlo Kerg, Sarthak Mittal, David Rolnick, Yoshua Bengio, Blake Aaron Richards, Guillaume Lajoie

NeurIPS 2022 Is a Modular Architecture Enough? Sarthak Mittal, Yoshua Bengio, Guillaume Lajoie

TMLR 2022 Lazy vs Hasty: Linearization in Deep Networks Impacts Learning Schedule Based on Example Difficulty Thomas George, Guillaume Lajoie, Aristide Baratin

ICMLW 2022 Lazy vs Hasty: Linearization in Deep Networks Impacts Learning Schedule Based on Example Difficulty Thomas George, Guillaume Lajoie, Aristide Baratin

ICML 2022 Multi-Scale Feature Learning Dynamics: Insights for Double Descent Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie

ICLRW 2022 On the Inadequacy of CKA as a Measure of Similarity in Deep Learning MohammadReza Davari, Stefan Horoi, Amine Natik, Guillaume Lajoie, Guy Wolf, Eugene Belilovsky

AISTATS 2021 Implicit Regularization via Neural Feature Alignment Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien

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

NeurIPSW 2020 Implicit Regularization via Neural Feature Alignment Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien

ICML 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

NeurIPS 2020 Untangling Tradeoffs Between Recurrence and Self-Attention in Artificial Neural Networks Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal ALIAS PARTH Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie

NeurIPSW 2019 Modelling Working Memory Using Deep Recurrent Reinforcement Learning Pravish Sainath, Pierre Bellec, Guillaume Lajoie

NeurIPS 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

NeurIPSW 2019 Recurrent Neural Networks Learn Robust Representations by Dynamically Balancing Compression and Expansion Matthew Farrell, Stefano Recanatesi, Guillaume Lajoie, Eric Shea-Brown