Publications

Andrew Melbourne

Hrvoje Bogunovic

Pim Moeskops

Xinjian Chen

Ernst Schwartz

M. Garvin

E. Robinson

E. Trucco

Michael Ebner

Yanwu Xu

Antonios Makropoulos

Adrien Desjardin

Tom Kamiel Magda Vercauteren

2016-12-31

Lecture Notes in Computer Science (publié)

Generalizable Features From Unsupervised Learning

Mehdi Mirza

Aaron Courville

Yoshua Bengio

Humans learn a predictive model of the world and use this model to reason about future events and the consequences of actions. In contrast t… (voir plus)o most machine predictors, we exhibit an impressive ability to generalize to unseen scenarios and reason intelligently in these settings. One important aspect of this ability is physical intuition(Lake et al., 2016). In this work, we explore the potential of unsupervised learning to find features that promote better generalization to settings outside the supervised training distribution. Our task is predicting the stability of towers of square blocks. We demonstrate that an unsupervised model, trained to predict future frames of a video sequence of stable and unstable block configurations, can yield features that support extrapolating stability prediction to blocks configurations outside the training set distribution

2016-12-31

ICLR.cc/2017/conference (Invite to Workshop Track)

openreview.net

GibbsNet: Iterative Adversarial Inference for Deep Graphical Models

R Devon Hjelm

Directed latent variable models that formulate the joint distribution as …

2016-12-31

Advances in Neural Information Processing Systems 30 (NIPS 2017) (publié)

Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics

M. Jorge Cardoso

Enzo Ferrante

Xavier Pennec

Adrian Dalca

Sarah Parisot

S. Joshi

Nematollah Batmanghelich

Aristeidis Sotiras

Mads Lenstrup Nielsen

Mert R. Sabuncu

Tom Fletcher

Li Shen

Stanley Durrleman

Stefan H. Sommer

2016-12-31

GRAIL/MFCA/MICGen@MICCAI (publié)

Hierarchical Methods of Moments

Matteo Ruffini

Guillaume Rabusseau

Borja Balle

Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models. Despite their theoretical appeal,… (voir plus) the applicability of these methods to real data is still limited due to a lack of robustness to model misspecification. In this paper we present a hierarchical approach to methods of moments to circumvent such limitations. Our method is based on replacing the tensor decomposition step used in previous algorithms with approximate joint diagonalization. Experiments on topic modeling show that our method outperforms previous tensor decomposition methods in terms of speed and model quality.

2016-12-31

Advances in Neural Information Processing Systems 30 (NIPS 2017) (publié)

Improved Training of Wasserstein GANs

Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserste… (voir plus)in GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms.

2016-12-31

Advances in Neural Information Processing Systems 30 (NIPS 2017) (publié)

Independently Controllable Factors

Valentin Thomas

Jules Pondard

Emmanuel Bengio

Marc Sarfati

Philippe Beaudoin

Marie-Jean Meurs

Joelle Pineau

Doina Precup

Yoshua Bengio

It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it rema… (voir plus)ins an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.

2016-12-31

arXiv (prépublication)

Independently Controllable Features

Valentin Thomas

2016-12-31

(publié)

Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis

M. Jorge Cardoso

Su-Lin Lee

Veronika Cheplygina

Simone Balocco

Diana Mateus

Guillaume Zahnd

Lena Maier-Hein

Stefanie Demirci

Eric Granger

Luc Duong

M. Carbonneau

Shadi N. Albarqouni

G. Carneiro

2016-12-31

CVII-STENT/LABELS@MICCAI (publié)

Modulating early visual processing by language

Jérémie Mary

Olivier Pietquin

It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view do… (voir plus)minates the current literature in computational models for language-vision tasks, where visual and linguistic input are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the \emph{entire visual processing} by linguistic input. Specifically, we condition the batch normalization parameters of a pretrained residual network (ResNet) on a language embedding. This approach, which we call MOdulated RESnet (\MRN), significantly improves strong baselines on two visual question answering tasks. Our ablation study shows that modulating from the early stages of the visual processing is beneficial.

2016-12-31

Advances in Neural Information Processing Systems 30 (NIPS 2017) (publié)

Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment

M. Jorge Cardoso

Fei Gao

BERNHARD KAINZ

T. Walsum

Kuangyu Shi

Kanwal K. Bhatia

R. Peter

Tom Kamiel Magda Vercauteren

Mauricio Reyes

Adrian Dalca

Roland Wiest

Wiro Niessen

B. Emmer

2016-12-31

CMMI/RAMBO/SWITCH@MICCAI (publié)