Samira Ebrahimi Kahou

Graham W. Taylor

Conditional text-to-image generation is an active area of research, with many possible applications. Existing research has primarily focused… (voir plus) on generating a single image from available conditioning information in one step. One practical extension beyond one-step generation is a system that generates an image iteratively, conditioned on ongoing linguistic input or feedback. This is significantly more challenging than one-step generation tasks, as such a system must understand the contents of its generated images with respect to the feedback history, the current feedback, as well as the interactions among concepts present in the feedback history. In this work, we present a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation. We show that our model is able to generate the background, add new objects, and apply simple transformations to existing objects. We believe our approach is an important step toward interactive generation. Code and data is available at: https://www.microsoft.com/en-us/research/project/generative-neural-visual-artist-geneva/.

2019-11-02

2019 IEEE/CVF International Conference on Computer Vision (ICCV) (publié)

doi.org

An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation

Vincent Michalski

Vikram Voleti

Anthony Ortiz

Pascal Vincent

Doina Precup

Batch normalization has been widely used to improve optimization in deep neural networks. While the uncertainty in batch statistics can act … (voir plus)as a regularizer, using these dataset statistics specific to the training set impairs generalization in certain tasks. Recently, alternative methods for normalizing feature activations in neural networks have been proposed. Among them, group normalization has been shown to yield similar, in some domains even superior performance to batch normalization. All these methods utilize a learned affine transformation after the normalization operation to increase representational power. Methods used in conditional computation define the parameters of these transformations as learnable functions of conditioning information. In this work, we study whether and where the conditional formulation of group normalization can improve generalization compared to conditional batch normalization. We evaluate performances on the tasks of visual question answering, few-shot learning, and conditional image generation.

2019-07-31

ArXiv (prépublication)

Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies

Sarath Chandar

Chinnadhurai Sankar

Eugene Vorontsov

Modelling long-term dependencies is a challenge for recurrent neural networks. This is primarily due to the fact that gradients vanish durin… (voir plus)g training, as the sequence length increases. Gradients can be attenuated by transition operators and are attenuated or dropped by activation functions. Canonical architectures like LSTM alleviate this issue by skipping information through a memory mechanism. We propose a new recurrent architecture (Non-saturating Recurrent Unit; NRU) that relies on a memory mechanism but forgoes both saturating activation functions and saturating gates, in order to further alleviate vanishing gradients. In a series of synthetic and real world tasks, we demonstrate that the proposed model is the only model that performs among the top 2 models across all tasks with and without long-term dependencies, when compared against a range of other architectures.

2019-07-17

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

Deep Learning recognizes weather and climate patterns

Karthik Kashinath

M. Prabhat

Mayur Mudigonda

Ankur Mahesh

Sookyung Kim

Yunjie Liu

B. Toms

Evan Racah

Christopher Beckham

Tegan Maharaj

Jim Biard

K. Kunkel

Dean Nesbit Williams

Travis O'Brien

M. Wehner

W. Collins

Keep Drawing It: Iterative language-based image generation and editing

Alaaeldin El-Nouby

Shikhar Sharma

Hannes Schulz

(Rex) Devon Hjelm

Layla El Asri

Graham W. Taylor

Conditional text-to-image generation approaches commonly focus on generating a single image in a single step. One practical extension beyond… (voir plus) one-step generation is an interactive system that generates an image iteratively, conditioned on ongoing linguistic input / feedback. This is significantly more challenging as such a system must understand and keep track of the ongoing context and history. In this work, we present a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation. We show that our model is able to generate the background, add new objects, apply simple transformations to existing objects, and correct previous mistakes. We believe our approach is an important step toward interactive generation.

2018-11-24

arXiv.org (prépublication)

dblp.uni-trier.de

ChatPainter: Improving Text to Image Generation using Dialogue

Shikhar Sharma

Dendi Suhubdy

Vincent Michalski

Synthesizing realistic images from text descriptions on a dataset like Microsoft Common Objects in Context (MS COCO), where each image can c… (voir plus)ontain several objects, is a challenging task. Prior work has used text captions to generate images. However, captions might not be informative enough to capture the entire image and insufficient for the model to be able to understand which objects in the images correspond to which words in the captions. We show that adding a dialogue that further describes the scene leads to significant improvement in the inception score and in the quality of generated images on the MS COCO dataset.

2018-01-01

ICLR (Workshop) (publié)

openreview.net

FigureQA: An Annotated Figure Dataset for Visual Reasoning

Adam Atkinson

Vincent Michalski

Ákos Kádár

Adam Trischler

We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are s… (voir plus)ynthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts. We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection. To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure. To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives. In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements. We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as a strong baseline. Preliminary results indicate that the task poses a significant machine learning challenge. We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data.

2018-01-01

ICLR.cc/2018/Conference (Invite to Workshop Track)

openreview.net

Towards Deep Conversational Recommendations

Raymond Li

Hannes Schulz

Vincent Michalski

Laurent Charlin

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendat… (voir plus)ion is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.

Theano: A Python framework for fast computation of mathematical expressions

Rami Al-rfou'

Guillaume Alain

Amjad Almahairi

Christof Angermüller

Dzmitry Bahdanau

Nicolas Ballas

Frédéric Bastien

Justin S. Bayer

A. Belikov

A. Belopolsky

Arnaud Bergeron

J. Bergstra

Valentin Bisson

Josh Bleecher Snyder

Nicolas Bouchard

Nicolas Boulanger-Lewandowski

Xavier Bouthillier

Alexandre De Brébisson

Olivier Breuleux … (voir 92 de plus)

pierre luc carrier

Kyunghyun Cho

Jan Chorowski

Paul F. Christiano

Tim Cooijmans

Marc-Alexandre Côté

Myriam Côté

Aaron Courville

Yann Dauphin

Olivier Delalleau

Julien Demouth

Guillaume Desjardins

Sander Dieleman

Laurent Dinh

M'elanie Ducoffe

Vincent Dumoulin

Dumitru Erhan

Ziye Fan

Orhan Firat

Mathieu Germain

Xavier Glorot

Ian J. Goodfellow

Matthew Graham

Caglar Gulcehre

Philippe Hamel

Iban Harlouchet

Jean-philippe Heng

Balázs Hidasi

Sina Honari

Arjun Jain

S'ebastien Jean

Kai Jia

Mikhail V. Korobov

Vivek Kulkarni

Alex Lamb

Pascal Lamblin

Eric P. Larsen

César Laurent

S. Lee

Simon-mark Lefrancois

Simon Lemieux

Nicholas Léonard

Zhouhan Lin

J. Livezey

Cory R. Lorenz

Jeremiah L. Lowin

Qianli M. Ma

Pierre-Antoine Manzagol

Olivier Mastropietro

R. McGibbon

Roland Memisevic

Bart van Merriënboer

Vincent Michalski

Mehdi Mirza

Alberto Orlandi

Razvan Pascanu

Mohammad Pezeshki

Colin Raffel

Daniel Renshaw

Matthew David Rocklin

Adriana Romero Soriano

Markus Dr. Roth

Peter Sadowski

John Salvatier

François Savard

Jan Schlüter

John D. Schulman

Gabriel Schwartz

Iulian V. Serban

Dmitriy Serdyuk

Samira Shabanian

Etienne Simon

Sigurd Spieckermann

S. Subramanyam

Jakub Sygnowski

Jérémie Tanguay

Gijs van Tulder

Joseph P. Turian

Sebastian Urban

Pascal Vincent

Francesco Visin

Harm de Vries

David Warde-Farley

Dustin J. Webb

M. Willson

Kelvin Xu

Lijun Xue

Li Yao

Saizheng Zhang

Ying Zhang

Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (voir plus)ntly. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.

2016-05-09

ArXiv (preprint)

Theano: A Python framework for fast computation of mathematical expressions

Rami Al-rfou'

Guillaume Alain

Amjad Almahairi

Christof Angermüller

Dzmitry Bahdanau

Nicolas Ballas

Frédéric Bastien

Justin S. Bayer

A. Belikov

A. Belopolsky

Arnaud Bergeron

James Bergstra

Valentin Bisson

Josh Bleecher Snyder

Nicolas Bouchard

Nicolas Boulanger-Lewandowski

Xavier Bouthillier

Alexandre De Brébisson

Olivier Breuleux … (voir 92 de plus)

pierre luc carrier

Kyunghyun Cho

Jan Chorowski

Paul F. Christiano

Tim Cooijmans

Marc-Alexandre Côté

Myriam Côté

Aaron Courville

Yann Dauphin

Olivier Delalleau

Julien Demouth

Guillaume Desjardins

Sander Dieleman

Laurent Dinh

M'elanie Ducoffe

Vincent Dumoulin

Dumitru Erhan

Ziye Fan

Orhan Firat

Mathieu Germain

Xavier Glorot

Ian G Goodfellow

Matthew Graham

Caglar Gulcehre

Philippe Hamel

Iban Harlouchet

Jean-philippe Heng

Balázs Hidasi

Sina Honari

Arjun Jain

Sébastien Jean

Kai Jia

Mikhail V. Korobov

Vivek Kulkarni

Alex Lamb

Pascal Lamblin

Eric Larsen

César Laurent

S. Lee

Simon-mark Lefrancois

Simon Lemieux

Nicholas Léonard

Zhouhan Lin

J. Livezey

Cory R. Lorenz

Jeremiah L. Lowin

Qianli M. Ma

Pierre-Antoine Manzagol

Olivier Mastropietro

R. McGibbon

Roland Memisevic

Bart van Merriënboer

Vincent Michalski

Mehdi Mirza

Alberto Orlandi

Razvan Pascanu

Mohammad Pezeshki

Colin Raffel

Daniel Renshaw

Matthew David Rocklin

Adriana Romero Soriano

Markus Dr. Roth

Peter Sadowski

John Salvatier

François Savard

Jan Schlüter

John D. Schulman

Gabriel Schwartz

Iulian V. Serban

Dmitriy Serdyuk

Samira Shabanian

Etienne Simon

Sigurd Spieckermann

S. Subramanyam

Jakub Sygnowski

Jérémie Tanguay

Gijs van Tulder

Joseph Turian

Sebastian Urban

Pascal Vincent

Francesco Visin

Harm de Vries

David Warde-Farley

Dustin J. Webb

M. Willson

Kelvin Xu

Lijun Xue

Li Yao

Saizheng Zhang

Ying Zhang

2016-05-09

ArXiv (prépublication)