All Research Areas
Research Areas
Year Published

520 Results

June 1, 2018

Colorless Green Recurrent Networks Dream Hierarchically

North American Chapter of the Association for Computational Linguistics (NAACL)

Recurrent neural networks (RNNs) have achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate here to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions.

By: Kristina Gulordava, Piotr Bojanowski, Edouard Grave, Tal Linzen, Marco Baroni
June 1, 2018

QuickEdit: Editing Text & Translations by Crossing Words Out

Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)

We propose a framework for computer-assisted text editing. It applies to translation post-editing and to paraphrasing. Our proposal relies on very simple interactions: a human editor modifies a sentence by marking tokens they would like the system to change.

By: David Grangier, Michael Auli
May 24, 2018

A Universal Music Translation Network


We present a method for translating music across musical instruments, genres, and styles. This method is based on a multi-domain wavenet autoencoder, with a shared encoder and a disentangled latent space that is trained end-to-end on waveforms.

By: Noam Mor, Lior Wolf, Adam Polyak, Yaniv Taigman
May 16, 2018

Glow: Graph Lowering Compiler Techniques for Neural Networks


This paper presents the design of Glow, a machine learning compiler for heterogeneous hardware. It is a pragmatic approach to compilation that enables the generation of highly optimized code for multiple targets. Glow lowers the traditional neural network dataflow graph into a two-phase strongly-typed intermediate representation.

By: Saleem Abdulrasool, Summer Deng, Roman Dzhabarov, Jordan Fix, James Hegeman, Roman Levenstein, Bert Maher, Satish Nadathur, Jakob Olesen, Jongsoo Park, Artem Rakhov, Nadav Rotem, Misha Smelyanskiy
May 16, 2018

Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems


Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming several challenges.

By: Ahmed Alkhateeb, Sam Alex, Paul Varkey, Ying Li, Qi Qu, Djordje Tujkovic
May 15, 2018

Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization

Proceedings of the IEEE

Motivated by a variety of applications—decentralized estimation in sensor networks, fitting models to massive data sets, and decentralized control of multi-robot systems, to name a few—significant advances have been made towards the development of robust, practical algorithms with theoretical performance guarantees. This paper presents an overview of recent work in this area.

By: Angelica Nedic, Alex Olshevsky, Mike Rabbat
May 8, 2018

Optimization Methods for Large-Scale Machine Learning

SIAM Review

This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications.

By: Leon Bottou, Frank E. Curtis, Jorge Nocedal
May 7, 2018

Advances in Pre-Training Distributed Word Representations

Language Resources and Evaluation Conference (LREC)

In this paper, we show how to train high-quality word vector representations by using a combination of known tricks that are however rarely used together.

By: Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, Armand Joulin
May 7, 2018

A Corpus for Multilingual Document Classification in Eight Languages

Language Resources and Evaluation Conference (LREC)

In this paper, we propose a new subset of the Reuters corpus with balanced class priors for eight languages. By adding Italian, Russian, Japanese and Chinese, we cover languages which are very different with respect to syntax, morphology, etc. We provide strong baselines for all language transfer directions using multilingual word and sentence embeddings respectively. Our goal is to offer a freely available framework to evaluate cross-lingual document classification, and we hope to foster by these means, research in this important area.

By: Holger Schwenk, Xian Li
May 2, 2018

Exploring the Limits of Weakly Supervised Pretraining


In this paper, we present a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images.

By: Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten