Research Area
Year Published

170 Results

April 4, 2018

Towards AI that can solve social dilemmas


Many scenarios involve a tension between individual interest and the interests of others. Such situations are called social dilemmas. Because of their ubiquity in economic and social interactions constructing agents that can solve them is of prime importance to researchers interested in multi-agent systems.

By: Alexander Peysakhovich, Adam Lerer

April 3, 2018

DesIGN: Design Inspiration from Generative Networks


Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation.

By: Othman Sbai, Mohamed Elhoseiny, Antoine Bordes, Yann LeCun, Camille Couprie

March 19, 2018

Randomized algorithms for distributed computation of principal component analysis and singular value decomposition

Advances in Computational Mathematics

Randomized algorithms provide solutions to two ubiquitous problems: (1) the distributed calculation of a principal component analysis or singular value decomposition of a highly rectangular matrix, and (2) the distributed calculation of a low-rank approximation (in the form of a singular value decomposition) to an arbitrary matrix.

By: Huamin Li, Yuval Kluger, Mark Tygert

March 14, 2018

Learning to Compute Word Embeddings On the Fly


We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained end-to-end for the downstream task. We show that this improves results against baselines where embeddings are trained on the end task for reading comprehension, recognizing textual entailment and language modeling.

By: Dzmitry Bahdanau, Tom Bosc, Stanislaw Jastrzebski, Edward Grefenstette, Pascal Vincent, Yoshua Bengio

March 12, 2018

Geometrical Insights for Implicit Generative Modeling


Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion.

By: Leon Bottou, Martin Arjovsky, David Lopez-Paz, Maxime Oquab

February 2, 2018

StarSpace: Embed All The Things!

Conference on Artificial Intelligence (AAAI)

We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi-relational graphs, and learning word, sentence or document level embeddings.

By: Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston

February 2, 2018

Efficient K-Shot Learning with Regularized Deep Networks

AAAI Conference on Artificial Intelligence (AAAI)

The problem of sub-optimality and over-fitting, is due in part to the large number of parameters (≈ 106) used in a typical deep convolutional neural network. To address these problems, we propose a simple yet effective regularization method for fine-tuning pre-trained deep networks for the task of k-shot learning.

By: Donghyun Yoo, Haoqi Fan, Vishnu Naresh Boddeti, Kris M. Kitani

February 2, 2018

Efficient Large-Scale Multi-Modal Classification

Conference on Artificial Intelligence (AAAI)

We investigate various methods for performing multi-modal fusion and analyze their trade-offs in terms of classification accuracy and computational efficiency.

By: Douwe Kiela, Edouard Grave, Armand Joulin, Tomas Mikolov

February 24, 2018

Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

International Symposium on High-Performance Computer Architecture (HPCA)

Facebook’s machine learning workloads are extremely diverse: services require many different types of models in practice. This paper describes the hardware and software infrastructure that supports machine learning at global scale.

By: Kim Hazelwood, Sarah Bird, David Brooks, Soumith Chintala, Utku Diril, Dmytro Dzhulgakov, Mohamed Fawzy, Bill Jia, Yangqing Jia, Aditya Kalro, James Law, Kevin Lee, Jason Lu, Pieter Noordhuis, Misha Smelyanskiy, Liang Xiong, Xiaodong Wang

November 27, 2018

Neural Separation of Observed and Unobserved Distributions

In this work, we tackle the scenario of extracting an unobserved distribution additively mixed with a signal from an observed (arbitrary) distribution. We introduce a new method: Neural Egg Separation – an iterative method that learns to separate the known distribution from progressively finer estimates of the unknown distribution.

By: Tavi Halperin, Ariel Ephrat, Yedid Hoshen