Publications

Chin-Wei Huang

Riashat Islam

Ryan Turner

Alexandre Lacoste

2017-10-13

ArXiv (preprint)

Bayesian Hypernetworks

Chin-Wei Huang

Riashat Islam

Ryan Turner

Alexandre Lacoste

We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork, h, is a neura… (see more)l network which learns to transform a simple noise distribution, p(e) = N(0,I), to a distribution q(t) := q(h(e)) over the parameters t of another neural network (the ``primary network). We train q with variational inference, using an invertible h to enable efficient estimation of the variational lower bound on the posterior p(t | D) via sampling. In contrast to most methods for Bayesian deep learning, Bayesian hypernets can represent a complex multimodal approximate posterior with correlations between parameters, while enabling cheap iid sampling of q(t). In practice, Bayesian hypernets provide a better defense against adversarial examples than dropout, and also exhibit competitive performance on a suite of tasks which evaluate model uncertainty, including regularization, active learning, and anomaly detection.

2017-10-13

ArXiv (preprint)

Bayesian Hypernetworks

Chin-Wei Huang

Riashat Islam

Ryan Turner

Alexandre Lacoste

2017-10-13

ArXiv (preprint)

Bayesian Hypernetworks

Chin-Wei Huang

Riashat Islam

Ryan Turner

Alexandre Lacoste

We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork, h, is a neura… (see more)l network which learns to transform a simple noise distribution, p(e) = N(0,I), to a distribution q(t) := q(h(e)) over the parameters t of another neural network (the ``primary network). We train q with variational inference, using an invertible h to enable efficient estimation of the variational lower bound on the posterior p(t | D) via sampling. In contrast to most methods for Bayesian deep learning, Bayesian hypernets can represent a complex multimodal approximate posterior with correlations between parameters, while enabling cheap iid sampling of q(t). In practice, Bayesian hypernets provide a better defense against adversarial examples than dropout, and also exhibit competitive performance on a suite of tasks which evaluate model uncertainty, including regularization, active learning, and anomaly detection.

2017-10-13

ArXiv (preprint)

Learning Independent Features with Adversarial Nets for Non-linear ICA

Philemon Brakel

Yoshua Bengio

Reliable measures of statistical dependence could potentially be useful tools for learning independent features and performing tasks like so… (see more)urce separation using Independent Component Analysis (ICA). Unfortunately, many of such measures, like the mutual information, are hard to estimate and optimize directly. We propose to learn independent features with adversarial objectives (Goodfellow et al. 2014, Arjovsky et al. 2017) which optimize such measures implicitly. These objectives compare samples from the joint distribution and the product of the marginals without the need to compute any probability densities. We also propose two methods for obtaining samples from the product of the marginals using either a simple resampling trick or a separate parametric distribution. Our experiments show that this strategy can easily be applied to different types of model architectures and solve both linear and non-linear ICA problems.

2017-10-13

ArXiv (preprint)

openreview.net

Learnable Explicit Density for Continuous Latent Space and Variational Inference

Chin-Wei Huang

Ahmed Touati

Laurent Dinh

Michal Drozdzal

Mohammad Havaei

Laurent Charlin

In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its correspon… (see more)ding posterior. First, we decompose the learning of VAEs into layerwise density estimation, and argue that having a flexible prior is beneficial to both sample generation and inference. Second, we analyze the family of inverse autoregressive flows (inverse AF) and show that with further improvement, inverse AF could be used as universal approximation to any complicated posterior. Our analysis results in a unified approach to parameterizing a VAE, without the need to restrict ourselves to use factorial Gaussians in the latent real space.

2017-10-06

ArXiv (preprint)

Neural Network Based Nonlinear Weighted Finite Automata

Tianyu Li

Guillaume Rabusseau

Doina Precup

Weighted finite automata (WFA) can expressively model functions defined over strings but are inherently linear models. Given the recent succ… (see more)esses of nonlinear models in machine learning, it is natural to wonder whether ex-tending WFA to the nonlinear setting would be beneficial. In this paper, we propose a novel model of neural network based nonlinearWFA model (NL-WFA) along with a learning algorithm. Our learning algorithm is inspired by the spectral learning algorithm for WFAand relies on a nonlinear decomposition of the so-called Hankel matrix, by means of an auto-encoder network. The expressive power of NL-WFA and the proposed learning algorithm are assessed on both synthetic and real-world data, showing that NL-WFA can lead to smaller model sizes and infer complex grammatical structures from data.

2017-09-13

ArXiv (preprint)

Predicting Future Disease Activity and Treatment Responders for Multiple Sclerosis Patients Using a Bag-of-Lesions Brain Representation

Andrew Doyle

Doina Precup

Douglas Arnold

Tal Arbel

2017-09-04

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 (published)

FiLM: Visual Reasoning with a General Conditioning Layer

Ethan Perez

Florian Strub

Harm de Vries

Vincent Dumoulin

We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence ne… (see more)ural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.

2017-09-01

ArXiv (preprint)

Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics

M. Cardoso

Tal Arbel

Enzo Ferrante

Xavier Pennec

Adrian Dalca

Sarah Parisot

S. Joshi

Nematollah Batmanghelich

Aristeidis Sotiras

Mads Lenstrup Nielsen

M. Sabuncu

Tom Fletcher

Li Shen

Stanley Durrleman

Stefan H. Sommer

2017-09-01

Lecture Notes in Computer Science (published)

World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions

Teng Long

Emmanuel Bengio

Ryan Lowe

Jackie Cheung

Doina Precup

Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading compr… (see more)ehension systems that can do the same. In this paper, we introduce a task and several models to drive progress towards this goal. In particular, we propose the task of rare entity prediction: given a web document with several entities removed, models are tasked with predicting the correct missing entities conditioned on the document context and the lexical resources. This task is challenging due to the diversity of language styles and the extremely large number of rare entities. We propose two recurrent neural network architectures which make use of external knowledge in the form of entity descriptions. Our experiments show that our hierarchical LSTM model performs significantly better at the rare entity prediction task than those that do not make use of external resources.

2017-09-01

Conference on Empirical Methods in Natural Language Processing (published)

End-to-end optimization of goal-driven and visually grounded dialogue systems

Florian Strub

Harm de Vries

Jérémie Mary

Bilal Piot

Olivier Pietquin

End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architec… (see more)tures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as a supervised learning problem, aiming at predicting the next utterance of a participant given the full history of the dialogue. This vision is too simplistic to render the intrinsic planning problem inherent to dialogue as well as its grounded nature , making the context of a dialogue larger than the sole history. This is why only chitchat and question answering tasks have been addressed so far using end-to-end architectures. In this paper, we introduce a Deep Reinforcement Learning method to optimize visually grounded task-oriented dialogues , based on the policy gradient algorithm. This approach is tested on a dataset of 120k dialogues collected through Mechanical Turk and provides encouraging results at solving both the problem of generating natural dialogues and the task of discovering a specific object in a complex picture.

2017-08-19

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (published)