Jonathan Binas

According to our database, Jonathan Binas authored at least 24 papers between 2012 and 2022.

Collaborative distances:

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

2022

Coordination Among Neural Modules Through a Shared Global Workspace.

Anirudh Goyal, Aniket Rajiv Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Curtis Mozer, Yoshua Bengio

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Out-of-Distribution Generalization via Risk Extrapolation (REx).

David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville

Proceedings of the 38th International Conference on Machine Learning, 2021

Reinforcement Learning with Random Delays.

Yann Bouteiller, Simon Ramstedt, Giovanni Beltrame, Christopher J. Pal, Jonathan Binas

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Out-of-Distribution Generalization via Risk Extrapolation (REx).

David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Rémi Le Priol, Aaron C. Courville

CoRR, 2020

DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction.

Yuhuang Hu, Jonathan Binas, Daniel Neil, Shih-Chii Liu, Tobi Delbrück

Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, 2020

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives.

Anirudh Goyal, Shagun Sodhani, Jonathan Binas, Xue Bin Peng, Sergey Levine, Yoshua Bengio

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations.

Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Denis Kazakov, Yoshua Bengio, Michael C. Mozer

CoRR, 2019

The Journey is the Reward: Unsupervised Learning of Influential Trajectories.

Jonathan Binas, Sherjil Ozair, Yoshua Bengio

CoRR, 2019

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations.

Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding.

Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio

CoRR, 2018

Generalization of Equilibrium Propagation to Vector Field Dynamics.

Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio

CoRR, 2018

Low-memory convolutional neural networks through incremental depth-first processing.

Jonathan Binas, Yoshua Bengio

CoRR, 2018

Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations.

Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio

CoRR, 2018

Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding.

Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Extending the Framework of Equilibrium Propagation to General Dynamics.

Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks.

Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Laurent Charlin, Chris Pal, Yoshua Bengio

CoRR, 2017

DDD17: End-To-End DAVIS Driving Dataset.

Jonathan Binas, Daniel Neil, Shih-Chii Liu, Tobi Delbrück

CoRR, 2017

2016

Precise deep neural network computation on imprecise low-power analog hardware.

Jonathan Binas, Daniel Neil, Giacomo Indiveri, Shih-Chii Liu, Michael Pfeiffer

CoRR, 2016

Deep counter networks for asynchronous event-based processing.

Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer

CoRR, 2016

Spiking analog VLSI neuron assemblies as constraint satisfaction problem solvers.

Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer

Proceedings of the IEEE International Symposium on Circuits and Systems, 2016

2015

Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing.

Peter U. Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, Shih-Chii Liu, Michael Pfeiffer

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Local structure helps learning optimized automata in recurrent neural networks.

Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

2014

Learning and stabilization of winner-take-all dynamics through interacting excitatory and inhibitory plasticity.

Jonathan Binas, Ueli Rutishauser, Giacomo Indiveri, Michael Pfeiffer

Frontiers Comput. Neurosci., 2014

2012

Systematic Construction of Finite State Automata Using VLSI Spiking Neurons.

Emre Neftci, Jonathan Binas, Elisabetta Chicca, Giacomo Indiveri, Rodney J. Douglas

Proceedings of the Biomimetic and Biohybrid Systems - First International Conference, 2012