Jonathan Binas

I am a postdoc at Mila, the Quebec Artificial Intelligence Institute.

Recent publications

  1. Jonathan Binas, Sherjil Ozair, Yoshua Bengio. The Journey is the Reward: Unsupervised Learning of Influential Trajectories. arXiv preprint arXiv:1905.09334, 2019. https://arxiv.org/abs/1905.09334
  2. Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Denis Kazakov, Yoshua Bengio, Michael C Mozer. State-reification networks: Improving generalization by modeling the distribution of hidden representations. Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3622-3631, 2019. http://proceedings.mlr.press/v97/lamb19a.html
  3. Anirudh Goyal, Shagun Sodhani, Jonathan Binas, Xue Bin Peng, Sergey Levine, Yoshua Bengio. Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives. arXiv preprint arXiv:1906.10667, 2019. https://arxiv.org/abs/1906.10667
  4. Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael Mozer, Chris Pal, Yoshua Bengio. Sparse attentive backtracking: Temporal credit assignment through reminding. Advances in Neural Information Processing Systems, 2018. http://papers.nips.cc/paper/7991-sparse-attentive-backtracking-temporal-credit-assignment-through-reminding
  5. Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio. Generalization of equilibrium propagation to vector field dynamics. arXiv preprint arXiv:1808.04873, 2018. https://arxiv.org/abs/1808.04873
  6. Jonathan Binas and Yoshua Bengio. Low-memory convolutional neural networks through incremental depth-first processing. arXiv preprint arXiv:1804.10727, 2018. https://arxiv.org/abs/1804.10727
  7. Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, and Yoshua Bengio. Fortified networks: improving the robustness of deep networks by modeling the manifold of hidden representations. arXiv preprint arXiv:1804.02485, 2018. https://arxiv.org/abs/1804.02485
  8. Jonathan Binas, Daniel Neil, Giacomo Indiveri, Shih-Chii Liu, and Michael Pfeiffer. Analog electronic deep networks for fast and efficient inference. SysML, 2018. https://www.sysml.cc/doc/179.pdf
  9. Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, and Yoshua Bengio. Extending the framework of equilibrium propagation to general dynamics. preprint, 2018. https://openreview.net/forum?id=BJ5V4ICIG
  10. Jonathan Binas. Brain-Inspired Models and Systems for Distributed Computation. PhD thesis, ETH Zurich, 2017. https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/216012/1/thesis.pdf
  11. Jonathan Binas, Daniel Neil, Shih-Chii Liu, and Tobi Delbruck. Ddd17: end-to-end davis driving dataset. arXiv preprint arXiv:1711.01458, 2017. https://arxiv.org/abs/1711.01458
  12. Jonathan Binas, Giacomo Indiveri, and Michael Pfeiffer. Deep counter networks for asynchronous event-based processing. arXiv preprint arXiv:1611.00710, 2016. https://arxiv.org/abs/1611.00710
  13. Jonathan Binas, Daniel Neil, Giacomo Indiveri, Shih-Chii Liu, and Michael Pfeiffer. Precise deep neural network computation on imprecise low-power analog hardware. arXiv preprint arXiv:1606.07786, 2016. https://arxiv.org/abs/1606.07786
  14. Jonathan Binas, Giacomo Indiveri, and Michael Pfeiffer. Local structure supports learning of deterministic behavior in recurrent neural networks. BMC neuroscience, 16(1):P195, 2015. https://bmcneurosci.biomedcentral.com/articles/10.1186/1471-2202-16-S1-P195
  15. Jonathan Binas, Giacomo Indiveri, and Michael Pfeiffer. Spiking analog vlsi neuron assemblies as constraint satisfaction problem solvers. In Circuits and Systems (ISCAS), 2016 IEEE International Symposium on, 2094–2097. IEEE, 2016. http://ieeexplore.ieee.org/abstract/document/7538992/
  16. Peter U Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, Shih-Chii Liu, and Michael Pfeiffer. Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. In Neural Networks (IJCNN), 2015 International Joint Conference on, 1–8. IEEE, 2015. http://ieeexplore.ieee.org/abstract/document/7280696/
  17. Jonathan Binas, Giacomo Indiveri, and Michael Pfeiffer. Local structure helps learning optimized automata in recurrent neural networks. In Neural Networks (IJCNN), 2015 International Joint Conference on, 1–7. IEEE, 2015. http://ieeexplore.ieee.org/abstract/document/7280714/
  18. Jonathan Binas, Ueli Rutishauser, Giacomo Indiveri, and Michael Pfeiffer. Learning and stabilization of winner-take-all dynamics through interacting excitatory and inhibitory plasticity. Frontiers in computational neuroscience, 8:68, 2014. https://www.frontiersin.org/articles/10.3389/fncom.2014.00068
  19. Emre Neftci, Jonathan Binas, Ueli Rutishauser, Elisabetta Chicca, Giacomo Indiveri, and Rodney J Douglas. Pnas plus significance statements. PNAS, 110(37):14827–14828, 2013. http://www.pnas.org/content/110/37/14827.short
  20. Emre Neftci, Jonathan Binas, Ueli Rutishauser, Elisabetta Chicca, Giacomo Indiveri, and Rodney J Douglas. Synthesizing cognition in neuromorphic electronic systems. Proceedings of the National Academy of Sciences, 110(37):E3468–E3476, 2013. http://www.pnas.org/content/110/37/E3468.short
  21. Emre Neftci, Jonathan Binas, Elisabetta Chicca, Giacomo Indiveri, and Rodney Douglas. Systematic construction of finite state automata using vlsi spiking neurons. Biomimetic and Biohybrid Systems, pages 382–383, 2012. https://link.springer.com/chapter/10.1007/978-3-642-31525-1_52
  22. Oliver Fenwick, Johannes K Sprafke, Jonathan Binas, Dmitry V Kondratuk, Francesco Di Stasio, Harry L Anderson, and Franco Cacialli. Linear and cyclic porphyrin hexamers as near-infrared emitters in organic light-emitting diodes. Nano letters, 11(6):2451–2456, 2011. http://pubs.acs.org/doi/abs/10.1021/nl2008778

© J. Binas, 2019