1 code implementation • 6 Sep 2021 • Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke
Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science.
no code implementations • 1 Jan 2021 • Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Bernhard Schölkopf, Michael Curtis Mozer, Hugo Larochelle, Christopher Pal, Yoshua Bengio
Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.
no code implementations • 30 Oct 2020 • Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St. Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gaétan Marceau Caron, Pierre Luc Carrier, Satya Ortiz-Gagné, Marc-Andre Rousseau, David Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Pal, Joanna Merckx, Eilif B. Muller, Yoshua Bengio
The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution.
2 code implementations • 2 Oct 2019 • Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Bernhard Schölkopf, Michael C. Mozer, Chris Pal, Yoshua Bengio
Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.
1 code implementation • 25 Sep 2019 • Chiheb Trabelsi, Olexa Bilaniuk, Ousmane Dia, Ying Zhang, Mirco Ravanelli, Jonathan Binas, Negar Rostamzadeh, Christopher J Pal
Using the Wall Street Journal Dataset, we compare our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Chiheb Trabelsi, Olexa Bilaniuk, Ousmane Dia, Ying Zhang, Mirco Ravanelli, Jonathan Binas, Negar Rostamzadeh, Christopher J Pal
Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain.
2 code implementations • ICLR 2020 • Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher Pal
We show that causal structures can be parameterized via continuous variables and learned end-to-end.
no code implementations • NeurIPS 2018 • Nan Rosemary Ke, Anirudh Goyal Alias Parth Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio
We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state.
no code implementations • 11 Sep 2018 • Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio
We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state.
no code implementations • ICLR 2018 • Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Laurent Charlin, Chris Pal, Yoshua Bengio
A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through every single step of the forward computation.
9 code implementations • ICLR 2018 • Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher J. Pal
Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models.
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no code implementations • 6 Jun 2016 • Yoshua Bengio, Benjamin Scellier, Olexa Bilaniuk, Joao Sacramento, Walter Senn
We find conditions under which a simple feedforward computation is a very good initialization for inference, after the input units are clamped to observed values.