Research Area
Year Published

634 Results

December 8, 2018

From Satellite Imagery to Disaster Insights

AI for Social Good Workshop at NeurIPS 2018

The use of satellite imagery has become increasingly popular for disaster monitoring and response. After a disaster, it is important to prioritize rescue operations, disaster response and coordinate relief efforts. These have to be carried out in a fast and efficient manner since resources are often limited in disaster affected areas and it’s extremely important to identify the areas of maximum damage. However, most of the existing disaster mapping efforts are manual which is time-consuming and often leads to erroneous results.

By: Jigar Doshi, Saikat Basu, Guan Pang

December 7, 2018

Bayesian Neural Networks using HackPPL with Application to User Location State Prediction

Bayesian Deep Learning Workshop at NeurIPS 2018

In this study, we present HackPPL as a probabilistic programming language in Facebook’s server-side language, Hack. One of the aims of our language is to support deep probabilistic modeling by providing a flexible interface for composing deep neural networks with encoded uncertainty and a rich inference engine.

By: Beliz Gokkaya, Jessica Ai, Michael Tingley, Yonglong Zhang, Ning Dong, Thomas Jiang, Anitha Kubendran, Aren Kumar

December 7, 2018

Stochastic Gradient Push for Distributed Deep Learning

Systems for Machine Learning Workshop at NeurIPS 2018

Large mini-batch parallel SGD is commonly used for distributed training of deep networks. Approaches that use tightly-coupled exact distributed averaging based on AllReduce are sensitive to slow nodes and high-latency communication. In this work we show the applicability of Stochastic Gradient Push (SGP) for distributed training.

By: Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Mike Rabbat

December 7, 2018

Rethinking floating point for deep learning

Systems for Machine Learning Workshop at NeurIPS 2018

We improve floating point to be more energy efficient than equivalent bit width integer hardware on a 28 nm ASIC process while retaining accuracy in 8 bits with a novel hybrid log multiply/linear add, Kulisch accumulation and tapered encodings from Gustafson’s posit format.

By: Jeff Johnson

December 6, 2018

Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis

Neural Information Processing Systems (NeurIPS)

Optimization algorithms that leverage gradient covariance information, such as variants of natural gradient descent (Amari, 1998), offer the prospect of yielding more effective descent directions. For models with many parameters, the covariance matrix they are based on becomes gigantic, making them inapplicable in their original form.

By: Thomas George, Cesar Laurent, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent

December 4, 2018

DeepFocus: Learned Image Synthesis for Computational Displays

ACM SIGGRAPH Asia 2018

In this paper, we introduce DeepFocus, a generic, end-to-end convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using only commonly available RGB-D images, enabling real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs.

By: Lei Xiao, Anton Kaplanyan, Alexander Fix, Matthew Chapman, Douglas Lanman

December 4, 2018

Non-Adversarial Mapping with VAEs

Neural Information Processing Systems (NeurIPS)

The study of cross-domain mapping without supervision has recently attracted much attention. Much of the recent progress was enabled by the use of adversarial training as well as cycle constraints. The practical difficulty of adversarial training motivates research into non-adversarial methods.

By: Yedid Hoshen

December 4, 2018

A2-Nets: Double Attention Networks

Neural Information Processing Systems (NeurIPS)

Learning to capture long-range relations is fundamental to image/video recognition. Existing CNN models generally rely on increasing depth to model such relations which is highly inefficient. In this work, we propose the “double attention block”, a novel component that aggregates and propagates informative global features from the entire spatio-temporal space of input images/videos, enabling subsequent convolution layers to access features from the entire space efficiently.

By: Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng

December 4, 2018

Improving Semantic Parsing for Task Oriented Dialog

Conversational AI Workshop at NeurIPS 2018

Semantic parsing using hierarchical representations has recently been proposed for task oriented dialog with promising results. In this paper, we present three different improvements to the model: contextualized embeddings, ensembling, and pairwise re-ranking based on a language model.

By: Arash Einolghozati, Panupong Pasupat, Sonal Gupta, Rushin Shah, Mrinal Mohit, Mike Lewis, Luke Zettlemoyer

December 3, 2018

Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes

Neural Information Processing Systems (NeurIPS)

While designing the state space of an MDP, it is common to include states that are transient or not reachable by any policy (e.g., in mountain car, the product space of speed and position contains configurations that are not physically reachable). This results in weakly-communicating or multi-chain MDPs. In this paper, we introduce TUCRL, the first algorithm able to perform efficient exploration-exploitation in any finite Markov Decision Process (MDP) without requiring any form of prior knowledge.

By: Ronan Fruit, Matteo Pirotta, Alessandro Lazaric