Jonathan Binas | DeepAI

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∙ 03/01/2021

Coordination Among Neural Modules Through a Shared Global Workspace

Deep learning has seen a movement away from representing examples with a...

14 Anirudh Goyal, et al. ∙

∙ 10/06/2020

Reinforcement Learning with Random Delays

Action and observation delays commonly occur in many Reinforcement Learn...

0 Simon Ramstedt, et al. ∙

∙ 05/18/2020

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

Neuromorphic event cameras are useful for dynamic vision problems under ...

20 Yuhuang Hu, et al. ∙

∙ 03/02/2020

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

Generalizing outside of the training distribution is an open challenge f...

25 David Krueger, et al. ∙

∙ 06/25/2019

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

Reinforcement learning agents that operate in diverse and complex enviro...

4 Anirudh Goyal, et al. ∙

∙ 05/26/2019

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

Machine learning promises methods that generalize well from finite label...

0 Alex Lamb, et al. ∙

∙ 05/22/2019

The Journey is the Reward: Unsupervised Learning of Influential Trajectories

Unsupervised exploration and representation learning become increasingly...

0 Jonathan Binas, et al. ∙

∙ 09/11/2018

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding

Learning long-term dependencies in extended temporal sequences requires ...

0 Nan Rosemary Ke, et al. ∙

∙ 08/14/2018

Generalization of Equilibrium Propagation to Vector Field Dynamics

The biological plausibility of the backpropagation algorithm has long be...

0 Benjamin Scellier, et al. ∙

∙ 04/28/2018

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

We introduce an incremental processing scheme for convolutional neural n...

0 Jonathan Binas, et al. ∙

∙ 04/07/2018

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

Deep networks have achieved impressive results across a variety of impor...

0 Alex Lamb, et al. ∙

∙ 11/07/2017

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

A major drawback of backpropagation through time (BPTT) is the difficult...

0 Nan Rosemary Ke, et al. ∙

∙ 11/04/2017

DDD17: End-To-End DAVIS Driving Dataset

Event cameras, such as dynamic vision sensors (DVS), and dynamic and act...

0 Jonathan Binas, et al. ∙

∙ 11/02/2016

Deep counter networks for asynchronous event-based processing

Despite their advantages in terms of computational resources, latency, a...

0 Jonathan Binas, et al. ∙

∙ 06/23/2016

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

There is an urgent need for compact, fast, and power-efficient hardware ...

0 Jonathan Binas, et al. ∙

∙ 11/02/2015

Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers

Solving constraint satisfaction problems (CSPs) is a notoriously expensi...

0 Jonathan Binas, et al. ∙

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