171 citations · 280 across the 12 of their papers we have counts for
4 papers · 1 filter
MLPerf Training Benchmark
Peter Mattson, Christine Cheng, Cody Coleman +34
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But M…
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
Peter Kraft, Daniel Kang, Deepak Narayanan +3
Systems for ML inference are widely deployed today, but they typically optimize ML inference workloads using techniques designed for conventional data serving workloads and miss cr…
Transfer of Adversarial Robustness Between Perturbation Types
Daniel Kang, Yi Sun, Tom Brown +2
We study the transfer of adversarial robustness of deep neural networks between different perturbation types. While most work on adversarial examples has focused on and…
Network Offloading Policies for Cloud Robotics: a Learning-based Approach
Sandeep Chinchali, Apoorva Sharma, James Harrison +6
Today's robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and obje…