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
AR/VR 
Human Computer Interaction & UX 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
Machine Learning 
Systems & Networking 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
Facebook AI Research 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 HjelmJanuary 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 PineauJanuary 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 SergeyDecember 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. ClarkeDecember 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
Economics & Computation 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
Natural Language Processing & Speech 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
Data Science