Search Results for author: Jonathan Binas

Found 20 papers, 8 papers with code

Coordination Among Neural Modules Through a Shared Global Workspace

no code implementations1 Mar 2021 Anirudh Goyal, Aniket Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Mozer, Yoshua Bengio

We explore the use of such a communication channel in the context of deep learning for modeling the structure of complex environments.

Reinforcement Learning with Random Delays

1 code implementation ICLR 2021 Simon Ramstedt, Yann Bouteiller, Giovanni Beltrame, Christopher Pal, Jonathan Binas

Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios.

Continuous Control

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

1 code implementation18 May 2020 Yuhuang Hu, Jonathan Binas, Daniel Neil, Shih-Chii Liu, Tobi Delbruck

The dataset was captured with a DAVIS camera that concurrently streams both dynamic vision sensor (DVS) brightness change events and active pixel sensor (APS) intensity frames.

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

3 code implementations2 Mar 2020 David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, Aaron Courville

Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world.

Domain Generalization

Retrieving Signals in the Frequency Domain with Deep Complex Extractors

1 code implementation25 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.

Audio Source Separation

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

1 code implementation ICLR 2020 Anirudh Goyal, Shagun Sodhani, Jonathan Binas, Xue Bin Peng, Sergey Levine, Yoshua Bengio

Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior.

Hierarchical Reinforcement Learning

The Journey is the Reward: Unsupervised Learning of Influential Trajectories

no code implementations22 May 2019 Jonathan Binas, Sherjil Ozair, Yoshua Bengio

Unsupervised exploration and representation learning become increasingly important when learning in diverse and sparse environments.

Representation Learning

Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding

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.

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding

no code implementations11 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.

Generalization of Equilibrium Propagation to Vector Field Dynamics

3 code implementations14 Aug 2018 Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio

The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists.

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

no code implementations28 Apr 2018 Jonathan Binas, Yoshua Bengio

We introduce an incremental processing scheme for convolutional neural network (CNN) inference, targeted at embedded applications with limited memory budgets.

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

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.

DDD17: End-To-End DAVIS Driving Dataset

1 code implementation4 Nov 2017 Jonathan Binas, Daniel Neil, Shih-Chii Liu, Tobi Delbruck

Event cameras, such as dynamic vision sensors (DVS), and dynamic and active-pixel vision sensors (DAVIS) can supplement other autonomous driving sensors by providing a concurrent stream of standard active pixel sensor (APS) images and DVS temporal contrast events.

Autonomous Driving

Deep counter networks for asynchronous event-based processing

no code implementations2 Nov 2016 Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer

Despite their advantages in terms of computational resources, latency, and power consumption, event-based implementations of neural networks have not been able to achieve the same performance figures as their equivalent state-of-the-art deep network models.

Precise neural network computation with imprecise analog devices

no code implementations23 Jun 2016 Jonathan Binas, Daniel Neil, Giacomo Indiveri, Shih-Chii Liu, Michael Pfeiffer

The operations used for neural network computation map favorably onto simple analog circuits, which outshine their digital counterparts in terms of compactness and efficiency.

Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers

no code implementations2 Nov 2015 Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer

Solving constraint satisfaction problems (CSPs) is a notoriously expensive computational task.

Cannot find the paper you are looking for? You can Submit a new open access paper.