626 Results
November 3, 2018
How Social Ties Influence Hurricane Evacuation Behavior
Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)
This work is the first of its kind, examining these phenomena across three major disasters in the United States—Hurricane Harvey, Hurricane Irma, and Hurricane Maria—using aggregated, de-identified data from over 1.5 million Facebook users.
By: Danaë Metaxa-Kakavouli, Paige Maas, Daniel P. Aldrich
Data Science November 3, 2018
RacerD: Compositional Static Race Detection
Conference on Object-Oriented Programming, Systems, Languages & Applications (OOPSLA)
Automatic static detection of data races is one of the most basic problems in reasoning about concurrency. We present RacerD—a static program analysis for detecting data races in Java programs which is fast, can scale to large code, and has proven effective in an industrial software engineering scenario.
By: Sam Blackshear, Nikos Gorogiannis, Peter O'Hearn, Ilya Sergey
Systems & Networking November 2, 2018
Dynamic Meta-Embeddings for Improved Sentence Representations
Empirical Methods in Natural Language Processing (EMNLP)
While one of the first steps in many NLP systems is selecting what pre-trained word embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves.
By: Douwe Kiela, Changhan Wang, Kyunghyun Cho
Facebook AI Research 
Natural Language Processing & Speech November 2, 2018
Do explanations make VQA models more predictable to a human?
Empirical Methods in Natural Language Processing (EMNLP)
A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable ‘explanations’ of their decision process, especially for interactive tasks like Visual Question Answering (VQA). In this work, we analyze if existing explanations indeed make a VQA model – its responses as well as failures – more predictable to a human.
By: Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, Devi Parikh
Computer Vision 
Machine Learning November 2, 2018
Jump to better conclusions: SCAN both left and right
Empirical Methods in Natural Language Processing (EMNLP)
Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models. Their initial experiments suggested that such models may fail because they lack the ability to extract systematic rules. Here, we take a closer look at SCAN and show that it does not always capture the kind of generalization that it was designed for.
By: Joost Bastings, Marco Baroni, Jason Weston, Kyunghyun Cho, Douwe KielaNovember 1, 2018
Social media governance: Can companies motivate voluntary rule following behavior among their users
Social Media Governance Workshop
The question of how to effectively enforce rules on social media mirrors the general question of how to enforce laws in society. One model for both governments and private companies is incapacitation—preventing people from taking particular actions.
By: Tom Tyler, Matt Katsaros, Tracey Meares, Sudhir Venkatesh
Human Computer Interaction & UX November 1, 2018
How agents see things: On visual representations in an emergent language game
Empirical Methods in Natural Language Processing (EMNLP)
There is growing interest in the language developed by agents interacting in emergent-communication settings. Earlier studies have focused on the agents’ symbol usage, rather than on their representation of visual input. In this paper, we consider the referential games of Lazaridou et al. (2017), and investigate the representations the agents develop during their evolving interaction.
By: Diane Bouchacourt, Marco BaroniNovember 1, 2018
Non-Adversarial Unsupervised Word Translation
Empirical Methods in Natural Language Processing (EMNLP)
In this paper, we make the observation that two sufficiently similar distributions can be aligned correctly with iterative matching methods.
By: Yedid Hoshen, Lior WolfNovember 1, 2018
Horizon: Facebook’s Open Source Applied Reinforcement Learning Platform
ArXiv
In this paper we present Horizon, Facebook’s open source applied reinforcement learning (RL) platform. Horizon is an end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don’t run in a simulator.
By: Jason Gauci, Edoardo Conti, Yitao Liang, Kittipat Virochsiri, Yuchen He, Zachary Kaden, Vivek Narayanan, Xiaohui YeOctober 31, 2018
Neural Compositional Denotational Semantics for Question Answering
Conference on Empirical Methods in Natural Language Processing (EMNLP)
Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics.
By: Nitish Gupta, Mike Lewis