Publications

Saliency Enhancement using Gradient Domain Edges Merging
Dominique Beaini
Sofiane Wozniak Achiche
Alexandre Duperre
Maxime Raison
In recent years, there has been a rapid progress in solving the binary problems in computer vision, such as edge detection which finds the b… (voir plus)oundaries of an image and salient object detection which finds the important object in an image. This progress happened thanks to the rise of deep-learning and convolutional neural networks (CNN) which allow to extract complex and abstract features. However, edge detection and saliency are still two different fields and do not interact together, although it is intuitive for a human to detect salient objects based on its boundaries. Those features are not well merged in a CNN because edges and surfaces do not intersect since one feature represents a region while the other represents boundaries between different regions. In the current work, the main objective is to develop a method to merge the edges with the saliency maps to improve the performance of the saliency. Hence, we developed the gradient-domain merging (GDM) which can be used to quickly combine the image-domain information of salient object detection with the gradient-domain information of the edge detection. This leads to our proposed saliency enhancement using edges (SEE) with an average improvement of the F-measure of at least 3.4 times higher on the DUT-OMRON dataset and 6.6 times higher on the ECSSD dataset, when compared to competing algorithm such as denseCRF and BGOF. The SEE algorithm is split into 2 parts, SEE-Pre for preprocessing and SEE-Post pour postprocessing.
2020-02-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks
Victor Schmidt
Mustafa Alghali
Kris Sankaran
Tianle Yuan
Yoshua Bengio
We introduce a conditional Generative Adversarial Network (cGAN) approach to generate cloud reflectance fields (CRFs) conditioned on large s… (voir plus)cale meteorological variables such as sea surface temperature and relative humidity. We show that our trained model can generate realistic CRFs from the corresponding meteorological observations, which represents a step towards a data-driven framework for stochastic cloud parameterization.
2020-02-10
ArXiv (prépublication)
arxiv.org
arxiv.org
Meta-learning framework with applications to zero-shot time-series forecasting
Boris Oreshkin
Dmitri Carpov
Nicolas Chapados
Yoshua Bengio
Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new… (voir plus) TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.
2020-02-07
ArXiv (prépublication)
doi.org
arxiv.org
Provably efficient reconstruction of policy networks
Bogdan Mazoure
Thang Doan
Tianyu Li
Vladimir Makarenkov
Joelle Pineau
Doina Precup
Guillaume Rabusseau
Recent research has shown that learning poli-cies parametrized by large neural networks can achieve significant success on challenging reinf… (voir plus)orcement learning problems. However, when memory is limited, it is not always possible to store such models exactly for inference, and com-pressing the policy into a compact representation might be necessary. We propose a general framework for policy representation, which reduces this problem to finding a low-dimensional embedding of a given density function in a separable inner product space. Our framework allows us to de-rive strong theoretical guarantees, controlling the error of the reconstructed policies. Such guaran-tees are typically lacking in black-box models, but are very desirable in risk-sensitive tasks. Our experimental results suggest that the reconstructed policies can use less than 10%of the number of parameters in the original networks, while incurring almost no decrease in rewards.
2020-02-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces.
Bogdan Mazoure
Thang Doan
Tianyu Li
Vladimir Makarenkov
Joelle Pineau
Doina Precup
Guillaume Rabusseau
We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional… (voir plus) embedding of the policy on a reproducing kernel Hilbert space (RKHS). The usage of RKHS based methods allows us to derive strong theoretical guarantees on the expected return of the reconstructed policy. Such guarantees are typically lacking in black-box models, but are very desirable in tasks requiring stability. We conduct several experiments on classic RL domains. The results confirm that the policies can be robustly embedded in a low-dimensional space while the embedded policy incurs almost no decrease in return.
Exploring Structural Inductive Biases in Emergent Communication
Agnieszka M Slowik
Abhinav Gupta
William L. Hamilton
M. Jamnik
S. Holden
Chris Pal
Human language and thought are characterized by the ability to systematically generate a potentially infinite number of complex structures (… (voir plus)e.g., sentences) from a finite set of familiar components (e.g., words). Recent works in emergent communication have discussed the propensity of artificial agents to develop a systematically compositional language through playing co-operative referential games. The degree of structure in the input data was found to affect the compositionality of the emerged communication protocols. Thus, we explore various structural priors in multi-agent communication and propose a novel graph referential game. We compare the effect of structural inductive bias (bag-of-words, sequences and graphs) on the emergence of compositional understanding of the input concepts measured by topographic similarity and generalization to unseen combinations of familiar properties. We empirically show that graph neural networks induce a better compositional language prior and a stronger generalization to out-of-domain data. We further perform ablation studies that show the robustness of the emerged protocol in graph referential games.
2020-02-04
arXiv.org (prépublication)
dblp.uni-trier.de
Cybersanté : les tentatives juridiques pour objectiver un domaine en pleine effervescence
Vincent Gautrais
Catherine Régis
Resting-state connectivity stratifies premanifest Huntington’s disease by longitudinal cognitive decline rate
Pablo Polosecki
Eduardo Castro
Irina Rish
Dorian Pustina
John H. Warner
Andrew Wood
Cristina Sampaio
Guillermo Cecchi
2020-01-27
Scientific Reports (publié)
doi.org
Using Simulated Data to Generate Images of Climate Change
Gautier Cosne
Adrien Juraver
Mélisande Teng
Victor Schmidt
Vahe Vardanyan
Alexandra Luccioni
Yoshua Bengio
Generative adversarial networks (GANs) used in domain adaptation tasks have the ability to generate images that are both realistic and perso… (voir plus)nalized, transforming an input image while maintaining its identifiable characteristics. However, they often require a large quantity of training data to produce high-quality images in a robust way, which limits their usability in cases when access to data is limited. In our paper, we explore the potential of using images from a simulated 3D environment to improve a domain adaptation task carried out by the MUNIT architecture, aiming to use the resulting images to raise awareness of the potential future impacts of climate change.
2020-01-26
ArXiv (prépublication)
arxiv.org
arxiv.org
Navigation in the Service of Enhanced Pose Estimation
Travis Manderson
Ran Cheng
David Meger
Gregory Dudek
2020-01-23
Springer Proceedings in Advanced Robotics (publié)
doi.org
Autism spectrum disorder
Catherine Lord
Traolach S. Brugha
Tony Charman
James Cusack
Guillaume Dumas
Thomas Frazier
Emily J. H. Jones
Rebecca M. Jones
Andrew Pickles
Matthew W. State
Julie Lounds Taylor
Jeremy Veenstra-VanderWeele
2020-01-16
Nature Reviews Disease Primers (publié)
doi.org
Optogenetic activation of parvalbumin and somatostatin interneurons selectively restores theta-nested gamma oscillations and oscillation-induced spike timing-dependent long-term potentiation impaired by amyloid β oligomers
Kyerl Park
Jaedong Lee
Hyun Jae Jang
Blake Richards
Michael M Kohl
Jeehyun Kwag
2020-01-15
BMC Biology (publié)
doi.org