activity
20162026
most citedDeep Reinforcement Learning for Cyber Security

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

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Showing 2023Show all

10 papers · 1 filter

cs.LG2023★ 6 cited

DMLR: Data-centric Machine Learning Research -- Past, Present and Future

Luis Oala, Manil Maskey, Lilith Bat-Leah +35

Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure developm…

cs.RO2023

VaPr: Variable-Precision Tensors to Accelerate Robot Motion Planning

Yu-Shun Hsiao, Siva Kumar Sastry Hari, Balakumar Sundaralingam +5

High-dimensional motion generation requires numerical precision for smooth, collision-free solutions. Typically, double-precision or single-precision floating-point (FP) formats ar…

cs.RO2023

RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance

Víctor Mayoral-Vilches, Jason Jabbour, Yu-Shun Hsiao +16

We introduce RobotPerf, a vendor-agnostic benchmarking suite designed to evaluate robotics computing performance across a diverse range of hardware platforms using ROS 2 as its com…

cs.RO2023

BERRY: Bit Error Robustness for Energy-Efficient Reinforcement Learning-Based Autonomous Systems

Zishen Wan, Nandhini Chandramoorthy, Karthik Swaminathan +3

Autonomous systems, such as Unmanned Aerial Vehicles (UAVs), are expected to run complex reinforcement learning (RL) models to execute fully autonomous position-navigation-time tas…

cs.AR2023

Leveraging Residue Number System for Designing High-Precision Analog Deep Neural Network Accelerators

Cansu Demirkiran, Rashmi Agrawal, Vijay Janapa Reddi +2

Achieving high accuracy, while maintaining good energy efficiency, in analog DNN accelerators is challenging as high-precision data converters are expensive. In this paper, we over…

cs.AR2023

ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design

Srivatsan Krishnan, Amir Yazdanbaksh, Shvetank Prakash +8

Machine learning is a prevalent approach to tame the complexity of design space exploration for domain-specific architectures. Using ML for design space exploration poses challenge…