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ORCIDBlake A. Richards
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- affiliation: McGill University, Montreal, QC, Canada
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2020 – today
- 2024
[c18]Nanda H. Krishna
, Colin Bredenberg, Daniel Levenstein, Blake Aaron Richards, Guillaume Lajoie:
Sufficient conditions for offline reactivation in recurrent neural networks. ICLR 2024- [c17]Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake Aaron Richards:
Synaptic Weight Distributions Depend on the Geometry of Plasticity. ICLR 2024 - [i20]David Raposo, Samuel Ritter, Blake A. Richards, Timothy P. Lillicrap, Peter Conway Humphreys, Adam Santoro:
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models. CoRR abs/2404.02258 (2024) - [i19]Karolis Jucys, George Adamopoulos, Mehrab Hamidi, Stephanie Milani, Mohammad Reza Samsami, Artem Zholus, Sonia Joseph, Blake A. Richards, Irina Rish, Özgür Simsek:
Interpretability in Action: Exploratory Analysis of VPT, a Minecraft Agent. CoRR abs/2407.12161 (2024) - [i18]Yizi Zhang, Yanchen Wang, Donato Jimenez-Beneto, Zixuan Wang, Mehdi Azabou, Blake A. Richards, Olivier Winter, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole L. Hurwitz:
Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution. CoRR abs/2407.14668 (2024) - 2023
- [j8]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) - [c16]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 - [c15]Chen Sun, Wannan Yang, Thomas Jiralerspong, Dane Malenfant, Benjamin Alsbury-Nealy, Yoshua Bengio, Blake A. Richards:
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL. NeurIPS 2023 - [c14]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 - [c13]Colin Bredenberg, Ezekiel Williams, Cristina Savin, Blake A. Richards, Guillaume Lajoie:
Formalizing locality for normative synaptic plasticity models. NeurIPS 2023 - [c12]Pingsheng Li, Jonathan Cornford, Arna Ghosh, Blake A. Richards:
Learning better with Dale's Law: A Spectral Perspective. NeurIPS 2023 - [i17]Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake A. Richards:
Synaptic Weight Distributions Depend on the Geometry of Plasticity. CoRR abs/2305.19394 (2023) - [i16]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 abs/2310.16046 (2023) - [i15]Kumar Krishna Agrawal, Arna Ghosh, Adam Oberman, Blake A. Richards:
Addressing Sample Inefficiency in Multi-View Representation Learning. CoRR abs/2312.10725 (2023) - 2022
- [j7]Blake A. Richards, Timothy P. Lillicrap:
The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics. Frontiers Comput. Sci. 4: 810358 (2022) - [j6]Annik Yalnizyan-Carson, Blake A. Richards:
Forgetting Enhances Episodic Control With Structured Memories. Frontiers Comput. Neurosci. 16: 757244 (2022) - [c11]Anthony GX-Chen, Veronica Chelu, Blake A. Richards, Joelle Pineau:
A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions. AAAI 2022: 6829-6837 - [c10]Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake A. Richards, Yoshua Bengio:
Towards Scaling Difference Target Propagation by Learning Backprop Targets. ICML 2022: 5968-5987 - [c9]Kumar Krishna Agrawal, Arnab Kumar Mondal, Arna Ghosh, Blake A. Richards:
$\alpha$-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay. NeurIPS 2022 - [c8]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 - [i14]Anthony GX-Chen, Veronica Chelu, Blake A. Richards, Joelle Pineau:
A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions. CoRR abs/2201.01836 (2022) - [i13]Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake A. Richards, Yoshua Bengio:
Towards Scaling Difference Target Propagation by Learning Backprop Targets. CoRR abs/2201.13415 (2022) - [i12]Arna Ghosh, Arnab Kumar Mondal, Kumar Krishna Agrawal, Blake A. Richards:
Investigating Power laws in Deep Representation Learning. CoRR abs/2202.05808 (2022) - [i11]Josh Abramson, Arun Ahuja, Federico Carnevale, Petko Georgiev, Alex Goldin, Alden Hung, Jessica Landon, Timothy P. Lillicrap, Alistair Muldal, Blake A. Richards, Adam Santoro, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan:
Evaluating Multimodal Interactive Agents. CoRR abs/2205.13274 (2022) - [i10]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 abs/2206.00823 (2022) - [i9]Giancarlo Kerg, Sarthak Mittal, David Rolnick, Yoshua Bengio, Blake A. Richards, Guillaume Lajoie:
On Neural Architecture Inductive Biases for Relational Tasks. CoRR abs/2206.05056 (2022) - [i8]Chen Sun, Wannan Yang, Benjamin Alsbury-Nealy, Yoshua Bengio, Blake A. Richards:
Contrastive introspection (ConSpec) to rapidly identify invariant steps for success. CoRR abs/2210.05845 (2022) - [i7]Anthony Zador, Blake A. Richards, Bence Ölveczky, Sean Escola, Yoshua Bengio, Kwabena Boahen, Matthew M. Botvinick, Dmitri B. Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad P. Körding, Alexei A. Koulakov, Yann LeCun, Timothy P. Lillicrap, Adam H. Marblestone, Bruno A. Olshausen, Alexandre Pouget, Cristina Savin, Terrence J. Sejnowski, Eero P. Simoncelli, Sara A. Solla, David Sussillo, Andreas S. Tolias, Doris Tsao:
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. CoRR abs/2210.08340 (2022) - [i6]Damjan Kalajdzievski, Ximeng Mao, Pascal Fortier-Poisson, Guillaume Lajoie, Blake A. Richards:
Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer. CoRR abs/2211.16607 (2022) - 2021
- [j5]Laura E. Suárez, Blake A. Richards, Guillaume Lajoie, Bratislav Misic:
Learning function from structure in neuromorphic networks. Nat. Mach. Intell. 3(9): 771-786 (2021) - [j4]Luke Y. Prince, Blake A. Richards:
The overfitted brain hypothesis. Patterns 2(5): 100268 (2021) - [c7]Jonathan Cornford, Damjan Kalajdzievski, Marco Leite, Amélie Lamarquette, Dimitri Michael Kullmann, Blake Aaron Richards:
Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units. ICLR 2021 - [c6]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: 778-782 - [c5]Pouya Bashivan, Reza Bayat, Adam Ibrahim, Kartik Ahuja, Mojtaba Faramarzi, Touraj Laleh, Blake A. Richards, Irina Rish:
Adversarial Feature Desensitization. NeurIPS 2021: 10665-10677 - [c4]Shahab Bakhtiari, Patrick J. Mineault, Timothy P. Lillicrap, Christopher C. Pack, Blake A. Richards:
The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning. NeurIPS 2021: 25164-25178 - [c3]Patrick J. Mineault, Shahab Bakhtiari, Blake A. Richards, Christopher C. Pack:
Your head is there to move you around: Goal-driven models of the primate dorsal pathway. NeurIPS 2021: 28757-28771 - [i5]Luke Y. Prince, Ellen Boven, Roy Henha Eyono, Arna Ghosh, Joe Pemberton, Franz Scherr, Claudia Clopath, Rui Ponte Costa, Wolfgang Maass, Blake A. Richards, Cristina Savin, Katharina Anna Wilmes:
CCN GAC Workshop: Issues with learning in biological recurrent neural networks. CoRR abs/2105.05382 (2021) - [i4]Nicholas Roy, Ingmar Posner, Tim D. Barfoot, Philippe Beaudoin, Yoshua Bengio, Jeannette Bohg, Oliver Brock, Isabelle Depatie, Dieter Fox, Daniel E. Koditschek, Tomás Lozano-Pérez, Vikash Mansinghka, Christopher J. Pal, Blake A. Richards, Dorsa Sadigh, Stefan Schaal, Gaurav S. Sukhatme, Denis Thérien, Marc Toussaint, Michiel van de Panne:
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence. CoRR abs/2110.15245 (2021) - 2020
- [c2]Jordan Guerguiev, Konrad P. Körding, Blake A. Richards:
Spike-based causal inference for weight alignment. ICLR 2020 - [i3]Pouya Bashivan, Blake A. Richards, Irina Rish:
Adversarial Feature Desensitization. CoRR abs/2006.04621 (2020)
2010 – 2019
- 2019
- [j3]Blake A. Richards:
Moving beyond reward prediction errors. Nat. Mach. Intell. 1(5): 204-205 (2019) - [i2]Jordan Guerguiev, Konrad P. Körding, Blake A. Richards:
Spike-based causal inference for weight alignment. CoRR abs/1910.01689 (2019) - 2018
- [j2]Nathan Insel, Jordan Guerguiev, Blake A. Richards:
Irrelevance by inhibition: Learning, computation, and implications for schizophrenia. PLoS Comput. Biol. 14(8) (2018) - [c1]Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy P. Lillicrap:
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. NeurIPS 2018: 9390-9400 - [i1]Sergey Bartunov, Adam Santoro, Blake A. Richards, Geoffrey E. Hinton, Timothy P. Lillicrap:
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. CoRR abs/1807.04587 (2018) - 2012
- [j1]John Vervaeke, Timothy P. Lillicrap, Blake A. Richards:
Relevance Realization and the Emerging Framework in Cognitive Science. J. Log. Comput. 22(1): 79-99 (2012)
Coauthor Index
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last updated on 2024-10-07 21:21 CEST by the dblp team
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