1.1k citations · 4.1k across the 92 of their papers we have counts for
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Neural Lander: Stable Drone Landing Control using Learned Dynamics
Guanya Shi, Xichen Shi, Michael O'Connell +5
Precise near-ground trajectory control is difficult for multi-rotor drones, due to the complex aerodynamic effects caused by interactions between multi-rotor airflow and the enviro…
Policy Gradient in Partially Observable Environments: Approximation and Convergence
Kamyar Azizzadenesheli, Yisong Yue, Animashree Anandkumar
Policy gradient is a generic and flexible reinforcement learning approach that generally enjoys simplicity in analysis, implementation, and deployment. In the last few decades, thi…
signSGD with Majority Vote is Communication Efficient And Fault Tolerant
Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli +1
Training neural networks on large datasets can be accelerated by distributing the workload over a network of machines. As datasets grow ever larger, networks of hundreds or thousan…
Surprising Negative Results for Generative Adversarial Tree Search
Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu +2
While many recent advances in deep reinforcement learning (RL) rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsuper…
Stochastic Activation Pruning for Robust Adversarial Defense
Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton +4
Neural networks are known to be vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threa…
signSGD: Compressed Optimisation for Non-Convex Problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli +1
Training large neural networks requires distributing learning across multiple workers, where the cost of communicating gradients can be a significant bottleneck. signSGD alleviates…