250 citations · 260 across the 8 of their papers we have counts for
8 papers
Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control
Devdhar Patel, Hava Siegelmann
Reinforcement learning (RL) is rapidly reaching and surpassing human-level control capabilities. However, state-of-the-art RL algorithms often require timesteps and reaction times…
Optimizing Attention and Cognitive Control Costs Using Temporally-Layered Architectures
Devdhar Patel, Terrence Sejnowski, Hava Siegelmann
The current reinforcement learning framework focuses exclusively on performance, often at the expense of efficiency. In contrast, biological control achieves remarkable performance…
Temporally Layered Architecture for Adaptive, Distributed and Continuous Control
Devdhar Patel, Joshua Russell, Francesca Walsh +3
We present temporally layered architecture (TLA), a biologically inspired system for temporally adaptive distributed control. TLA layers a fast and a slow controller together to ac…
QuickNets: Saving Training and Preventing Overconfidence in Early-Exit Neural Architectures
Devdhar Patel, Hava Siegelmann
Deep neural networks have long training and processing times. Early exits added to neural networks allow the network to make early predictions using intermediate activations in the…
Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks
Weihao Tan, Devdhar Patel, Robert Kozma
Spiking neural networks (SNNs) have great potential for energy-efficient implementation of Deep Neural Networks (DNNs) on dedicated neuromorphic hardware. Recent studies demonstrat…
Locally Connected Spiking Neural Networks for Unsupervised Feature Learning
Daniel J. Saunders, Devdhar Patel, Hananel Hazan +2
In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks. We introduce a method for learning image features by…