Research Area
Year Published

286 Results

January 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 Pineau
January 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 Hjelm
January 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 Pineau
December 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
December 8, 2018

From Satellite Imagery to Disaster Insights

AI for Social Good Workshop at NeurIPS 2018

The use of satellite imagery has become increasingly popular for disaster monitoring and response. After a disaster, it is important to prioritize rescue operations, disaster response and coordinate relief efforts. These have to be carried out in a fast and efficient manner since resources are often limited in disaster affected areas and it’s extremely important to identify the areas of maximum damage. However, most of the existing disaster mapping efforts are manual which is time-consuming and often leads to erroneous results.

By: Jigar Doshi, Saikat Basu, Guan Pang
December 7, 2018

Stochastic Gradient Push for Distributed Deep Learning

Systems for Machine Learning Workshop at NeurIPS 2018

Large mini-batch parallel SGD is commonly used for distributed training of deep networks. Approaches that use tightly-coupled exact distributed averaging based on AllReduce are sensitive to slow nodes and high-latency communication. In this work we show the applicability of Stochastic Gradient Push (SGP) for distributed training.

By: Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Mike Rabbat
December 7, 2018

Rethinking floating point for deep learning

Systems for Machine Learning Workshop at NeurIPS 2018

We improve floating point to be more energy efficient than equivalent bit width integer hardware on a 28 nm ASIC process while retaining accuracy in 8 bits with a novel hybrid log multiply/linear add, Kulisch accumulation and tapered encodings from Gustafson’s posit format.

By: Jeff Johnson
December 6, 2018

Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis

Neural Information Processing Systems (NeurIPS)

Optimization algorithms that leverage gradient covariance information, such as variants of natural gradient descent (Amari, 1998), offer the prospect of yielding more effective descent directions. For models with many parameters, the covariance matrix they are based on becomes gigantic, making them inapplicable in their original form.

By: Thomas George, Cesar Laurent, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent
December 4, 2018

Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog

Conversational AI Workshop at NeurIPS 2018

In this paper, we investigate the performance of three different methods for cross-lingual transfer learning, namely (1) translating the training data, (2) using cross-lingual pre-trained embeddings, and (3) a novel method of using a multilingual machine translation encoder as contextual word representations.

By: Sebastian Schuster, Sonal Gupta, Rushin Shah, Mike Lewis
December 4, 2018

Non-Adversarial Mapping with VAEs

Neural Information Processing Systems (NeurIPS)

The study of cross-domain mapping without supervision has recently attracted much attention. Much of the recent progress was enabled by the use of adversarial training as well as cycle constraints. The practical difficulty of adversarial training motivates research into non-adversarial methods.

By: Yedid Hoshen