4 citations · 5 across the 7 of their papers we have counts for
7 papers
Generalizable Reinforcement Learning with Biologically Inspired Hyperdimensional Occupancy Grid Maps for Exploration and Goal-Directed Path Planning
Shay Snyder, Ryan Shea, Andrew Capodieci +2
Real-time autonomous systems utilize multi-layer computational frameworks to perform critical tasks such as perception, goal finding, and path planning. Traditional methods impleme…
Asynchronous Neuromorphic Optimization with Lava
Shay Snyder, Sumedh R. Risbud, Maryam Parsa
Performing optimization with event-based asynchronous neuromorphic systems presents significant challenges. Intel's neuromorphic computing framework, Lava, offers an abstract appli…
Transductive Spiking Graph Neural Networks for Loihi
Shay Snyder, Victoria Clerico, Guojing Cong +4
Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements…
Neuromorphic Bayesian Optimization in Lava
Shay Snyder, Sumedh R. Risbud, Maryam Parsa
The ever-increasing demands of computationally expensive and high-dimensional problems require novel optimization methods to find near-optimal solutions in a reasonable amount of t…
Object Motion Sensitivity: A Bio-inspired Solution to the Ego-motion Problem for Event-based Cameras
Shay Snyder, Hunter Thompson, Md Abdullah-Al Kaiser +3
Neuromorphic (event-based) image sensors draw inspiration from the human-retina to create an electronic device that can process visual stimuli in a way that closely resembles its b…
Simulate Less, Expect More: Bringing Robot Swarms to Life via Low-Fidelity Simulations
Ricardo Vega, Kevin Zhu, Sean Luke +2
This paper proposes a novel methodology for addressing the simulation-reality gap for multi-robot swarm systems. Rather than immediately try to shrink or `bridge the gap' anytime a…