1.1k citations · 7.8k across the 180 of their papers we have counts for
13 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…
Born Again Neural Networks
Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen +2
Knowledge Distillation (KD) consists of transferring “knowledge†from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacit…
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…
Question Type Guided Attention in Visual Question Answering
Yang Shi, Tommaso Furlanello, Sheng Zha +1
Visual Question Answering (VQA) requires integration of feature maps with drastically different structures and focus of the correct regions. Image descriptors have structures at mu…
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…
Active Learning with Partial Feedback
Peiyun Hu, Zachary C. Lipton, Anima Anandkumar +1
While many active learning papers assume that the learner can simply ask for a label and receive it, real annotation often presents a mismatch between the form of a label (say, one…
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…
Efficient Exploration through Bayesian Deep Q-Networks
Kamyar Azizzadenesheli, Animashree Anandkumar
We study reinforcement learning (RL) in high dimensional episodic Markov decision processes (MDP). We consider value-based RL when the optimal Q-value is a linear function of d-dim…
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs
Forough Arabshahi, Sameer Singh, Animashree Anandkumar
Neural programming involves training neural networks to learn programs, mathematics, or logic from data. Previous works have failed to achieve good generalization performance, espe…