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Year Published

533 Results

July 10, 2018

Modeling Others using Oneself in Multi-Agent Reinforcement Learning

International Conference on Machine Learning (ICML)

We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility.

By: Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus
July 9, 2018

Experience developing and deploying concurrency analysis at Facebook

Static Analysis Symposium

This paper tells the story of the development of RacerD, a static program analysis for detecting data races that is in production at Facebook. The technical details of RacerD are described in a separate paper; we concentrate here on how the project unfolded from a human point of view.

By: Peter O'Hearn
July 9, 2018

Continuous Reasoning: Scaling the Impact of Formal Methods

Logic in Computer Science

This paper describes work in continuous reasoning, where formal reasoning about a (changing) codebase is done in a fashion which mirrors the iterative, continuous model of software development that is increasingly practiced in industry. We suggest that advances in continuous reasoning will allow formal reasoning to scale to more programs, and more programmers.

By: Peter O'Hearn
July 9, 2018

Mixed batches and symmetric discriminators for GAN training

International Conference on Machine Learning (ICML)

We propose to feed the discriminator with mixed batches of true and fake samples, and train it to predict the ratio of true samples in the batch.

By: Thomas Lucas, Corentin Tallec, Jakob Verbeek, Yann Ollivier
July 9, 2018

Composable Planning with Attributes

International Conference on Machine Learning (ICML)

We propose a method that learns a policy for transitioning between “nearby” sets of attributes, and maintains a graph of possible transitions.

By: Amy Zhang, Adam Lerer, Sainbayar Sukhbaatar, Rob Fergus, Arthur Szlam
July 7, 2018

Reconstructing Scenes with Mirror and Glass Surfaces


We introduce a fully automatic pipeline that allows us to reconstruct the geometry and extent of planar glass and mirror surfaces while being able to distinguish between the two.

By: Thomas Whelan, Michael Goesele, Steven J. Lovegrove, Julian Straub, Simon Green, Richard Szeliski, Steven Butterfield, Shobhit Verma, Richard Newcombe
July 7, 2018

Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations


We show that the gradient descent algorithm provides an implicit regularization effect in the learning of over-parameterized matrix factorization models and one-hidden-layer neural networks with quadratic activations.

By: Yuanzhi Li, Tengyu Ma, Hongyang Zhang
July 1, 2018

We Need a Testability Transformation Semantics

16th International Conference on Software Engineering and Formal Methods

This paper briefly reviews Testability Transformation, its formal definition, and the open problem of constructing a set of formal test adequacy semantics to underpin the current practice of deploying transformations to help testing and verification activities.

By: Mark Harman
June 29, 2018

Understanding the Loss Surface of Neural Networks for Binary Classification

International Conference on Machine Learning (ICML)

Here we focus on the training performance of neural networks for binary classification, and provide conditions under which the training error is zero at all local minima of appropriately chosen surrogate loss functions.

By: Shiyu Liang, Ruoyu Sun, Yixuan Li, R. Srikant
June 28, 2018

Hardware Remediation At Scale

International Conference on Dependable Systems and Networks (DSN)

Large scale services have automated hardware remediation to maintain the infrastructure availability at a healthy level. In this paper, we share the current remediation flow at Facebook, and how it is being monitored.

By: Fan (Fred) Lin, Matt Beadon, Harish Dattatraya Dixit, Gautham Vunnam, Amol Desai, Sriram Sankar