Research Area
Year Published

634 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 10, 2018

Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima

International Conference on Machine Learning (ICML)

We consider the problem of learning a one-hidden-layer neural network with non-overlapping convolutional layer and ReLU activation function, i.e., f(Z; w, a) = Σj ajσ(wT Zj), in which both the convolutional weights w and the output weights a are parameters to be learned.

By: Simon S. Du, Jason D. Lee, Yuandong Tian, Barnabás Póczos, Aarti Singh

July 10, 2018

Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks

International Conference on Machine Learning (ICML)

In this paper, we introduce the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences.

By: Brenden Lake, Marco Baroni

July 10, 2018

Hierarchical Text Generation and Planning for Strategic Dialogue

International Conference on Machine Learning (ICML)

We introduce an approach to learning representations of messages in dialogues by maximizing the likelihood of subsequent sentences and actions, which decouples the semantics of the dialogue utterance from its linguistic realization.

By: Denis Yarats, Mike Lewis

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 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 7, 2018

Reconstructing Scenes with Mirror and Glass Surfaces

ACM SIGGRAPH

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

arXiv

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