Research Area
Year Published

265 Results

July 11, 2018

Convergent TREE BACKUP and RETRACE with Function Approximation

International Conference on Machine Learning (ICML)

In this work, we show that the TREE BACKUP and RETRACE algorithms are unstable with linear function approximation, both in theory and in practice with specific examples.

By: Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent
July 11, 2018

Fitting New Speakers Based on a Short Untranscribed Sample

International Conference on Machine Learning (ICML)

We present a method that is designed to capture a new speaker from a short untranscribed audio sample.

By: Eliya Nachmani, Adam Polyak, Yaniv Taigman, Lior Wolf
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 10, 2018

Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry

International Conference on Machine Learning (ICML)

We are concerned with the discovery of hierarchical relationships from large-scale unstructured similarity scores. For this purpose, we study different models of hyperbolic space and find that learning embeddings in the Lorentz model is substantially more efficient than in the Poincaré-ball model.

By: Maximilian Nickel, Douwe Kiela
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 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