Jonathan Binas

Jonathan has a background in Physics, has worked in Neuroscience, Electronics (developed analog hardware to run neural networks), and is currently a postdoc with Yoshua Bengio, working on deep and reinforcement learning problems. His current interests include modular policies, brain-inspired learning algorithms, and alternative computing substrates.

Recent publications

  1. Reinforcement Learning with Random Delays. Ramstedt, Simon; Bouteiller, Yann; Beltrame, Giovanni; Pal, Christopher; Binas, Jonathan. arXiv preprint arXiv:2010.02966. 2020. https://arxiv.org/abs/2010.02966
  2. DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction. Hu, Yuhuang; Binas, Jonathan; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi. ITSC. 2020. https://arxiv.org/abs/2005.08605
  3. Out-of-distribution generalization via risk extrapolation (rex). Krueger, David; Caballero, Ethan; Jacobsen, Joern-Henrik; Zhang, Amy; Binas, Jonathan; Priol, Remi Le; Courville, Aaron. arXiv preprint arXiv:2003.00688. 2020. https://arxiv.org/abs/2003.00688
  4. Reinforcement learning with competitive ensembles of information-constrained primitives. Goyal, Anirudh; Sodhani, Shagun; Binas, Jonathan; Peng, Xue Bin; Levine, Sergey; Bengio, Yoshua. ICLR. 2020. https://openreview.net/forum?id=ryxgJTEYDr
  5. The Journey is the Reward: Unsupervised Learning of Influential Trajectories. Binas, Jonathan; Ozair, Sherjil; Bengio, Yoshua. ICML Workshop: ERL. 2019. https://arxiv.org/abs/1905.09334
  6. Retrieving Signals with Deep Complex Extractors. Trabelsi, Chiheb; Bilaniuk, Olexa; Dia, Ousmane; Zhang, Ying; Ravanelli, Mirco; Binas, Jonathan; Rostamzadeh, Negar; Pal, Christopher J. NeurIPS Workshop: Deep Inverse Models. 2019. https://openreview.net/forum?id=H1x22Xn5Ur
  7. Reinforcement Learning for Sustainable Agriculture. Binas, Jonathan; Luginbuehl, Leonie; Bengio, Yoshua. ICML Climate Change Workshop. 2019. https://www.climatechange.ai/CameraReady/40/CameraReadySubmission/Yield_optimization.pdf
  8. Lagrangian dynamics of dendritic microcircuits enables real-time backpropagation of errors. Dold, Dominik; Kungl, Akos F; Sacramento, João; Petrovici, Mihai A; Schindler, Kaspar; Binas, Jonathan; Bengio, Yoshua; Senn, Walter. Cosyne. 2019. http://www.kip.uni-heidelberg.de/Veroeffentlichungen/download.php/6269/temp/3855.pdf
  9. State-reification networks: Improving generalization by modeling the distribution of hidden representations. Lamb, Alex; Binas, Jonathan; Goyal, Anirudh; Subramanian, Sandeep; Mitliagkas, Ioannis; Kazakov, Denis; Bengio, Yoshua; Mozer, Michael C. ICML. 2019. http://proceedings.mlr.press/v97/lamb19a.html
  10. Analogue electronic neural network. Binas, Jonathan; Neil, Daniel. US Patent App. 16/078,769. 2019. https://patents.google.com/patent/US20190050720A1/en
  11. Fortified networks: Improving the robustness of deep networks by modeling the manifold of hidden representations. Lamb, Alex; Binas, Jonathan; Goyal, Anirudh; Serdyuk, Dmitriy; Subramanian, Sandeep; Mitliagkas, Ioannis; Bengio, Yoshua. arXiv preprint arXiv:1804.02485. 2018. https://arxiv.org/abs/1804.02485
  12. Generalization of equilibrium propagation to vector field dynamics. Scellier, Benjamin; Goyal, Anirudh; Binas, Jonathan; Mesnard, Thomas; Bengio, Yoshua. ICLR Workshop. 2018. https://arxiv.org/abs/1808.04873
  13. Fully discretized training of neural networks through direct feedback. Mesnard, Thomas; Vignoud, Gaëtan; Binas, Jonathan; Bengio, Yoshua. preprint. 2018.
  14. Low-memory convolutional neural networks through incremental depth-first processing. Binas, Jonathan; Bengio, Yoshua. arXiv preprint arXiv:1804.10727. 2018. https://arxiv.org/abs/1804.10727
  15. Analog electronic deep networks for fast and efficient inference. Binas, Jonathan; Neil, Daniel; Indiveri, Giacomo; Liu, Shih-Chii; Pfeiffer, Michael. Proc Conf. Syst. Mach. Learning (SysML). 2018. https://mlsys.org/Conferences/2019/doc/2018/179.pdf
  16. Sparse attentive backtracking: Temporal credit assignment through reminding. Ke, Nan Rosemary; Goyal, Anirudh; Bilaniuk, Olexa; Binas, Jonathan; Mozer, Michael C; Pal, Chris; Bengio, Yoshua. NeurIPS. 2018. http://papers.nips.cc/paper/7991-sparse-attentive-backtracking-temporal-credit-assignment-through-reminding
  17. Brain-inspired models and systems for distributed computation. Binas, Jonathan. Thesis; ETH Zurich. 2017. https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/216012/1/thesis.pdf
  18. DDD17: End-to-end DAVIS driving dataset. Binas, Jonathan; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi. ICML Workshop: ITS. 2017. https://arxiv.org/abs/1711.01458
  19. Precise neural network computation with imprecise analog devices. Binas, Jonathan; Neil, Daniel; Indiveri, Giacomo; Liu, Shih-Chii; Pfeiffer, Michael. arXiv preprint arXiv:1606.07786. 2016. https://arxiv.org/abs/1606.07786
  20. Deep counter networks for asynchronous event-based processing. Binas, Jonathan; Indiveri, Giacomo; Pfeiffer, Michael. arXiv preprint arXiv:1611.00710. 2016. https://arxiv.org/abs/1611.00710
  21. Spiking analog vlsi neuron assemblies as constraint satisfaction problem solvers. Binas, Jonathan; Indiveri, Giacomo; Pfeiffer, Michael. ISCAS. 2016. https://ieeexplore.ieee.org/abstract/document/7538992/
  22. Local structure supports learning of deterministic behavior in recurrent neural networks. Binas, Jonathan; Indiveri, Giacomo; Pfeiffer, Michael. BMC Neuroscience. 2015. https://link.springer.com/article/10.1186/1471-2202-16-S1-P195
  23. Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. Diehl, Peter U; Neil, Daniel; Binas, Jonathan; Cook, Matthew; Liu, Shih-Chii; Pfeiffer, Michael. IJCNN. 2015. https://ieeexplore.ieee.org/abstract/document/7280696/
  24. Local structure helps learning optimized automata in recurrent neural networks. Binas, Jonathan; Indiveri, Giacomo; Pfeiffer, Michael. IJCNN. 2015. https://ieeexplore.ieee.org/abstract/document/7280714/
  25. Learning and stabilization of winner-take-all dynamics through interacting excitatory and inhibitory plasticity. Binas, Jonathan; Rutishauser, Ueli; Indiveri, Giacomo; Pfeiffer, Michael. Frontiers in computational neuroscience. 2014. https://www.frontiersin.org/articles/10.3389/fncom.2014.00068/full
  26. Synthesizing cognition in neuromorphic electronic systems. Neftci, Emre; Binas, Jonathan; Rutishauser, Ueli; Chicca, Elisabetta; Indiveri, Giacomo; Douglas, Rodney J. Proceedings of the National Academy of Sciences (PNAS). 2013. https://www.pnas.org/content/110/37/E3468.short
  27. Systematic Construction of Finite State Automata Using VLSI Spiking Neurons. Neftci, Emre; Binas, Jonathan; Chicca, Elisabetta; Indiveri, Giacomo; Douglas, Rodney. Biomimetic and Biohybrid Systems. 2012. https://link.springer.com/chapter/10.1007/978-3-642-31525-1_52
  28. Linear and cyclic porphyrin hexamers as near-infrared emitters in organic light-emitting diodes. Fenwick, Oliver; Sprafke, Johannes K; Binas, Jonathan; Kondratuk, Dmitry V; Di Stasio, Francesco; Anderson, Harry L; Cacialli, Franco. Nano letters. 2011. https://pubs.acs.org/doi/abs/10.1021/nl2008778

© J. Binas, 2020