Over the last year, ten teams from around the world have been participating in the DARPA Subterranean challenges. These challenges are designed to test drones and robots on how they perform in hazardous physical environments…
Machines are a long way from robustly solving open-world perception-control tasks, such as first-person view (FPV) aerial navigation. While recent advances in end-to-end Machine Learning, especially Imitation and Reinforcement Learning appear promising, they are constrained…
A statistical adaptive procedure to automatically schedule the learning rate for deep learning with a broad family of stochastic gradient methods.
This library implements the algorithms in our paper for computing robust radii for different smoothing distributions against different adversaries.
Quickr explores cost-effective data analytics at scale, breaking new ground in research on offering approximate answers for complex ad-hoc queries.
The Deep Learning group advances the state-of-the-art in deep learning to achieve general intelligence. We develop algorithms, models, and systems in deep supervised and unsupervised learning, deep reinforcement learning, and neural-symbolic reasoning, and then pursue breakthroughs in computer vision,…
