Research Area
Year Published

697 Results

May 4, 2019

Lost in Style: Gaze-driven Adaptive Aid for VR Navigation

ACM CHI Conference on Human Factors in Computing Systems

We introduce a novel adaptive aid that maintains the effectiveness of traditional aids, while equipping designers and users with the controls of how often help is displayed.

By: Rawan Alghofaili, Yasuhito Sawahata, Haikun Huang, Hsueh-Cheng Wang, Takaaki Shiratori, Lap-Fai Yu

May 1, 2019

Learning graphs from data: A signal representation perspective

IEEE Signal Processing Magazine

In this tutorial overview, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective.

By: Xiaowen Dong, Dorina Thanou, Michael Rabbat, Pascal Frossard

April 30, 2019

Learning Word Vectors for 157 Languages

International Conference on Language Resources and Evaluation (LREC)

Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages.

By: Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, Tomas Mikolov

April 28, 2019

Inverse Path Tracing for Joint Material and Lighting Estimation

Computer Vision and Pattern Recognition (CVPR)

Modern computer vision algorithms have brought significant advancement to 3D geometry reconstruction. However, illumination and material reconstruction remain less studied, with current approaches assuming very simplified models for materials and illumination. We introduce Inverse Path Tracing, a novel approach to jointly estimate the material properties of objects and light sources in indoor scenes by using an invertible light transport simulation.

By: Dejan Azinović, Tzu-Mao Li, Anton S. Kaplanyan, Matthias Nießner

April 19, 2019

Cutting the Cord: Soft Haptic Devices without a Pressure Source

International Conference on Soft Robotics (RoboSoft)

We present a class of pneumatic haptic devices that use the input motion from a user to pump fluid in a closed pneumatic circuit, meaning that the pneumatic devices require no external pressure supply.

By: Nathan Usevitch, Andrew Stanley
Areas: AR/VR

April 15, 2019

Active Exploration in Markov Decision Processes

International Conference on Artificial Intelligence and Statistics (AISTATS)

We introduce the active exploration problem in Markov decision processes (MDPs). Each state of the MDP is characterized by a random value and the learner should gather samples to estimate the mean value of each state as accurately as possible.

By: Jean Tarbouriech, Alessandro Lazaric

April 15, 2019

Optimizing over a Restricted Policy Class in MDPs

AISTATS

We address the problem of finding an optimal policy in a Markov decision process under a restricted policy class defined by the convex hull of a set of base policies. This problem is of great interest in applications in which a number of reasonably good (or safe) policies are already known and we are interested in optimizing in their convex hull.

By: Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis

April 12, 2019

Presto: SQL on Everything

IEEE International Conference on Data Engineering (ICDE)

Presto is an open source distributed query engine that supports much of the SQL analytics workload at Facebook. Presto is designed to be adaptive, flexible, and extensible.

By: Raghav Sethi, Martin Traverso, Dain Sundstrom, David Phillips, Wenlei Xie, Yutian Sun, Nezih Yigitbasi, Haozhun Jin, Eric Hwang, Nileema Shingte, Christopher Berner

April 2, 2019

Bandana: Using Non-Volatile Memory for Storing Deep Learning Models

Conference on Systems and Machine Learning (SysML)

Typical large-scale recommender systems use deep learning models that are stored on a large amount of DRAM. These models often rely on embeddings, which consume most of the required memory. We present Bandana, a storage system that reduces the DRAM footprint of embeddings, by using Non-volatile Memory (NVM) as the primary storage medium, with a small amount of DRAM as cache.

By: Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim Hazelwood, Asaf Cidon, Sachin Katti

April 1, 2019

PyTorch-BigGraph: A Large-scale Graph Embedding System

Conference on Systems and Machine Learning (SysML)

We present PyTorch-BigGraph (PBG), an embedding system that incorporates several modifications to traditional multi-relation embedding systems that allow it to scale to graphs with billions of nodes and trillions of edges.

By: Adam Lerer, Ledell Wu, Jiajun Shen, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich