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,…
Generating Natural Questions About an Image CryptoNets: Machine Learning Inference on Encrypted Data Everest: Deploying Verified-Secure Implementations in the HTTPS Ecosystem Molecular Programming
