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Found 20 papers, 8 papers with code
Coordination Among Neural Modules Through a Shared Global Workspace
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
Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios.
DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction
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)
Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world.
Retrieving Signals in the Frequency Domain with Deep Complex Extractors
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.
Retrieving Signals with Deep Complex Extractors
Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain.
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Machine learning promises methods that generalize well from finite labeled data.
The Journey is the Reward: Unsupervised Learning of Influential Trajectories
Unsupervised exploration and representation learning become increasingly important when learning in diverse and sparse environments.
Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding
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
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
The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists.
Low-memory convolutional neural networks through incremental depth-first processing
We introduce an incremental processing scheme for convolutional neural network (CNN) inference, targeted at embedded applications with limited memory budgets.
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 important tasks.
Extending the Framework of Equilibrium Propagation to General Dynamics
The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists.
Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks
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
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.
Deep counter networks for asynchronous event-based processing
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
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
Solving constraint satisfaction problems (CSPs) is a notoriously expensive computational task.
