activity
20162026
most citedDeep Reinforcement Learning for Cyber Security

595 citations · 1.9k across the 91 of their papers we have counts for

collaborators
Showing 2021Show all

17 papers · 1 filter

cs.LG2021★ 6 cited

The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage

Daniel Galvez, Greg Diamos, Juan Ciro +7

The People's Speech is a free-to-download 30,000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-B…

cs.RO2021

Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems

Zishen Wan, Aqeel Anwar, Yu-Shun Hsiao +3

Learning-based navigation systems are widely used in autonomous applications, such as robotics, unmanned vehicles and drones. Specialized hardware accelerators have been proposed f…

cs.RO2021★ 3 cited

Roofline Model for UAVs:A Bottleneck Analysis Tool for Designing Compute Systems for Autonomous Drones

Srivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj +2

We present a bottleneck analysis tool for designing compute systems for autonomous Unmanned Aerial Vehicles (UAV). The tool provides insights by exploiting the fundamental relation…

cs.LG2021★ 173 cited

MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation

Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving prov…

cs.RO2021★ 4 cited

AutoSoC: Automating Algorithm-SOC Co-design for Aerial Robots

Srivatsan Krishnan, Thierry Tambe, Zishen Wan +1

Aerial autonomous machines (Drones) has a plethora of promising applications and use cases. While the popularity of these autonomous machines continues to grow, there are many chal…

cs.RO2021★ 17 cited

GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients

Brian Plancher, Sabrina M. Neuman, Radhika Ghosal +2

We introduce GRiD: a GPU-accelerated library for computing rigid body dynamics with analytical gradients. GRiD was designed to accelerate the nonlinear trajectory optimization subp…