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550 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 11, 2018

Learning Diffusion using Hyperparameters

International Conference on Machine Learning (ICML)

In this paper we advocate for a hyperparametric approach to learn diffusion in the independent cascade (IC) model. The sample complexity of this model is a function of the number of edges in the network and consequently learning becomes infeasible when the network is large.

By: Dimitris Kalimeris, Yaron Singer, Karthik Subbian, Udi Weinsberg
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

Optimizing the Latent Space of Generative Networks

International Conference on Machine Learning (ICML)

The goal of this paper is to disentangle the contribution of these two factors to the success of GANs. In particular, we introduce Generative Latent Optimization (GLO), a framework to train deep convolutional generators using simple reconstruction losses.

By: Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam
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