300 Results
May 12, 2019
Provably Accelerated Randomized Gossip Algorithms
IEEE International Conference on Acoustics, Speech, and Signal Processing
In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem.
By: Nicolas Loizou, Michael Rabbat, Peter Richtárik
Facebook AI Research May 4, 2019
Quasi-Hyperbolic Momentum and Adam for Deep Learning
International Conference on Learning Representations (ICLR)
Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other algorithms, and we characterize the set of two-state optimization algorithms that QHM can recover.
By: Jerry Ma, Denis Yarats
Computer Vision 
Machine Learning 
Natural Language Processing & Speech May 1, 2019
Learning graphs from data: A signal representation perspective
IEEE Signal Processing Magazine
In this tutorial overview, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective.
By: Xiaowen Dong, Dorina Thanou, Michael Rabbat, Pascal FrossardJanuary 30, 2019
Large-Scale Visual Relationship Understanding
AAAI Conference on Artificial Intelligence (AAAI)
Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of
January 30, 2019
Memorize or generalize? Searching for a compositional RNN in a haystack
IJCAI-ECAI Workshop: Architectures and Evaluation for Generality, Autonomy & Progress in AI
Neural networks are very powerful learning systems, but they do not readily generalize from one task to the other. This is partly due to the fact that they do not learn in a compositional way, that is, by discovering skills that are shared by different tasks, and recombining them to solve new problems. In this paper, we explore the compositional generalization capabilities of recurrent neural networks (RNNs).
By: Adam Liška, Germán Kruszewski, Marco BaroniJanuary 28, 2019
Combined Reinforcement Learning via Abstract Representations
AAAI Conference on Artificial Intelligence (AAAI)
In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions.
By: Vincent Francois-Lavet, Yoshua Bengio, Doina Precup, Joelle PineauJanuary 18, 2019
On-line Adaptative Curriculum Learning for GANs
AAAI Conference on Artificial Intelligence (AAAI)
Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and mode collapse still persist. In this paper, we build on existing work in the area by proposing a novel framework for training the generator against an ensemble of discriminator networks, which can be seen as a one-student/multiple-teachers setting. We formalize this problem within the full-information adversarial bandit framework, where we evaluate the capability of an algorithm to select mixtures of discriminators for providing the generator with feedback during learning.
By: Thang Doan, João Monteiro, Isabela Albuquerque, Bodgan Mazoure, Audrey Durand, Joelle Pineau, R. Devon HjelmJanuary 18, 2019
Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks
AAAI Conference on Artificial Intelligence (AAAI)
There are many reasons to expect an ability to reason in terms of objects to be a crucial skill for any generally intelligent agent. Indeed, recent machine learning literature is replete with examples of the benefits of object-like representations: generalization, transfer to new tasks, and interpretability, among others. However, in order to reason in terms of objects, agents need a way of discovering and detecting objects in the visual world – a task which we call unsupervised object detection.
By: Eric Crawford, Joelle PineauDecember 14, 2018
PyText: A seamless path from NLP research to production
We introduce PyText – a deep learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale.
By: Ahmed Aly, Kushal Lakhotia, Shicong Zhao, Mrinal Mohit, Barlas Oğuz, Abhinav Arora, Sonal Gupta, Christopher Dewan, Stef Nelson-Lindall, Rushin ShahDecember 11, 2018
The Costs of Overambitious Seeding of Social Products
International Conference on Complex Networks and their Applications
Product-adoption scenarios are often theoretically modeled as “influence-maximization” (IM) problems, where people influence one another to adopt and the goal is to find a limited set of people to “seed” so as to maximize long-term adoption. In many IM models, if there is no budgetary limit on seeding, the optimal approach involves seeding everybody immediately. Here, we argue that this approach can lead to suboptimal outcomes for “social products” that allow people to communicate with one another.
By: Shankar Iyer, Lada Adamic
Data Science