Research Area
Year Published

634 Results

January 1, 2020

Designing Safe Spaces for Virtual Reality

Ethics in Design and Communication 2020

Virtual Reality (VR) designers accept the ethical responsibilities of removing a user’s entire world and superseding it with a fabricated reality. These unique immersive design challenges are intensified when virtual experiences become public and socially-driven. As female VR designers in 2018, we see an opportunity to fold the language of consent into the design practice of virtual reality—as a means to design safe, accessible, virtual spaces.

Publication will be made available in 2020.

By: Michelle Cortese, Andrea Zeller

February 16, 2019

Machine Learning at Facebook: Understanding Inference at the Edge

IEEE International Symposium on High-Performance Computer Architecture (HPCA)

This paper takes a data-driven approach to present the opportunities and design challenges faced by Facebook in order to enable machine learning inference locally on smartphones and other edge platforms.

By: Carole-Jean Wu, David Brooks, Kevin Chen, Douglas Chen, Sy Choudhury, Marat Dukhan, Kim Hazelwood, Eldad Isaac, Yangqing Jia, Bill Jia, Tommer Leyvand, Hao Lu, Yang Lu, Lin Qiao, Brandon Reagen, Joe Spisak, Fei Sun, Andrew Tulloch, Peter Vajda, Xiaodong Wang, Yanghan Wang, Bram Wasti, Yiming Wu, Ran Xian, Sungjoo Yoo, Peizhao Zhang

January 28, 2019

Combined Reinforcement Learning via Abstract Representations

AAAI Conference on Artificial Intelligence (AAAI)

In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions.

By: Vincent Francois-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau

January 18, 2019

On-line Adaptative Curriculum Learning for GANs

AAAI Conference on Artificial Intelligence (AAAI)

Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and mode collapse still persist. In this paper, we build on existing work in the area by proposing a novel framework for training the generator against an ensemble of discriminator networks, which can be seen as a one-student/multiple-teachers setting. We formalize this problem within the full-information adversarial bandit framework, where we evaluate the capability of an algorithm to select mixtures of discriminators for providing the generator with feedback during learning.

By: Thang Doan, João Monteiro, Isabela Albuquerque, Bodgan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm

January 18, 2019

Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks

AAAI Conference on Artificial Intelligence (AAAI)

There are many reasons to expect an ability to reason in terms of objects to be a crucial skill for any generally intelligent agent. Indeed, recent machine learning literature is replete with examples of the benefits of object-like representations: generalization, transfer to new tasks, and interpretability, among others. However, in order to reason in terms of objects, agents need a way of discovering and detecting objects in the visual world – a task which we call unsupervised object detection.

By: Eric Crawford, Joelle Pineau

January 14, 2019

A True Positives Theorem for a Static Race Detector

Principles of Programming Languages (POPL)

RacerD is a static race detector that has been proven to be effective in engineering practice: it has seen thousands of data races fixed by developers before reaching production, and has supported the migration of Facebook’s Android app rendering infrastructure from a single-threaded to a multi-threaded architecture.

By: Nikos Gorogiannis, Peter O'Hearn, Ilya Sergey

December 17, 2018

Compact Dielectric Elastomer Linear Actuators

Advanced Functional Materials 2018

The design and fabrication of a rolled dielectric elastomer actuator is described and the parametric dependence of the displacement and blocked force on the actuator geometry, elastomer layer thickness, voltage, and number of turns is analyzed.

By: Huichan Zhao, Aftab M. Hussain, Mihai Duduta, Daniel M. Vogt, Robert J. Wood, David R. Clarke
Areas: AR/VR

December 15, 2018

Multiplicative Pacing Equilibria in Auction Markets

The 14th Conference on Web and Internet Economics (WINE)

Budgets play a significant role in real-world sequential auction markets such as those implemented by Internet companies. To maximize the value provided to auction participants, spending is smoothed across auctions so budgets are used for the best opportunities. This paper considers a smoothing procedure that relies on pacing multipliers: for each bidder, the platform applies a factor between 0 and 1 that uniformly scales the bids across all auctions.

By: Vincent Conitzer, Christian Kroer, Eric Sodomka, Nicolas Stier

December 14, 2018

PyText: A seamless path from NLP research to production

By: Ahmed Aly, Kushal Lakhotia, Shicong Zhao, Mrinal Mohit, Barlas Oguz, Abhinav Arora, Sonal Gupta, Christopher Dewan, Stef Nelson-Lindall, Rushin Shah

December 11, 2018

The Costs of Overambitious Seeding of Social Products

International Conference on Complex Networks and their Applications

Product-adoption scenarios are often theoretically modeled as “influence-maximization” (IM) problems, where people influence one another to adopt and the goal is to find a limited set of people to “seed” so as to maximize long-term adoption. In many IM models, if there is no budgetary limit on seeding, the optimal approach involves seeding everybody immediately. Here, we argue that this approach can lead to suboptimal outcomes for “social products” that allow people to communicate with one another.

By: Shankar Iyer, Lada Adamic