455 citations · 971 across the 43 of their papers we have counts for
11 papers · 1 filter
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…
A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model
Tan Nguyen, Nhat Ho, Ankit Patel +3
Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Deconvolutional Generative Model (DGM), a new probabilistic gener…
Open Vocabulary Learning on Source Code with a Graph-Structured Cache
Milan Cvitkovic, Badal Singh, Anima Anandkumar
Machine learning models that take computer program source code as input typically use Natural Language Processing (NLP) techniques. However, a major challenge is that code is writt…
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…
Probabilistic FastText for Multi-Sense Word Embeddings
Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar
We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information. In particular, we repre…