CosmoClub: 17-01-2018

Date: January 17th 2018

Speaker: François Orieux (L2S, Centrale-Supélec)

Title: Semi-unsupervised Bayesian Convex Image Restoration with Location Mixture of Gaussian


Convex image restoration is a major field in inverse problems. The problem is often addressed by hand-tuning hyper-parameters. We propose an incremental contribution about a Bayesian approach where a convex field is constructed via Location Mixture of Gaussian and the estimator computed with a fast MCMC algorithm. Main contributions are a new field with several operator avoiding crosslike artifacts and a fallback sampling algorithm to prevent numerical errors. Results, in comparison to standard supervised results, have equivalent quality in a quasi-unsupervised approach and go with uncertainty quantification.


CosmoClub: 08-12-2017

Date: December 8th 2017

Speaker: Emilie Chouzenoux (Université Paris-Est)

Title: Proximity operator computation for video restoration [slides]


Proximal methods have gained much interest for solving large-scale possibly non smooth optimization problems. When dealing with complicated convex functions, the expression of the proximity operator is however often non explicit and it thus needs to be determined numerically. We show in this work how block-coordinate algorithms can be designed to perform this task. We deduce also distributed optimization strategies allowing us to implement our solutions on multicore architectures. Applications of these methods to video restoration of old TV sequences illustrate the good performance of the proposed algorithms.

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Learning in Astrophysics day

Learning in Astrophysics day

Date: January the 26th, 2017

Organizer:  Joana Frontera-Pons  <>


Local information

CEA Saclay is around 23 km South of Paris. The astrophysics division (DAp) is located at the CEA site at Orme des Merisiers, which is around 1 km South of the main CEA campus. See for detailed information on how to arrive.

On January the 26th, 2017, we organize the third day on machine learning in astrophysics at DAp, CEA Saclay. 


All talks are taking place at DAp, Salle Galilée (Building 713)

10:00 - 10:45h. Marc Duranton  (CEA Saclay)
10:45 - 11:15h. Rémi Flamary (Université Nice-Sophia Antipolis)
11:15 - 11:45h. Christoph Ernst René Schäfer  (EPFL)

12:00 - 13:30h. Lunch

13:30 - 14:00h. Emille Ishida  (Laboratoire de Physique de Clermont)
14:00 - 14:30h. Silvia Villa (Politecnico di Milano)                                                            
14:30 - 15:00h. Arthur Pajot (LIP6)
15:00 - 15:30h. Morgan Schmitz  (CEA Saclay - CosmoStat)

15:30 - 16:00h. Coffe break

16:00 - 17:00h. Round table


Deep Learning for Physical Processes:  Incorporating Prior Scientific Knowledge 

Arthur Pajot (LIP6)

We consider  the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive paradigm could begin to challenge more traditional approaches elaborated over the years in fields like maths or physics. However, despite considerable successes in a variety of application domains, the machine learning field is not yet ready to handle the level of complexity required by such problems. Using an example application, namely Sea Surface Temperature Prediction, we show how general background knowledge gained from physics could be used as a guideline for designing efficient Deep Learning models.

Wasserstein dictionary Learning

Morgan Schmitz (CEA Saclay - CosmoStat)

Optimal Transport theory enables the definition of a distance across the set of measures on any given space. This Wasserstein distance naturally accounts for geometric warping between measures (including, but not exclusive to, images). We introduce a new, Optimal Transport-based representation learning method in close analogy with the usual Dictionary Learning problem. This approach typically relies on a matrix dot-product between the learned dictionary and the codes making up the new representation. The relationship between atoms and data is thus ultimately linear. 

We instead use automatic differentiation to derive gradients of the Wasserstein barycenter operator, and we learn a set of atoms and barycentric weights from the data in an unsupervised fashion. Since our data is reconstructed as Wasserstein barycenters of our learned atoms, we can make full use of the attractive properties of the Optimal Transport geometry. In particular, our representation allows for non-linear relationships between atoms and data.




CosmoClub: 15-11-2017

Date: November 15th 2017

Speaker: Eleonora Villa (SISSA)

Title: Theoretical systematics in galaxy clustering in LCDM and beyond


We study the impact of neglecting lensing magnification in galaxy clustering analyses for future galaxy surveys, considering the ΛCDM model and two extensions: massive neutrinos and modifications of General Relativity. Our study focuses on the biases on the constraints and on the estimation of the cosmological parameters. Our results show that the information present in the lensing contribution does improve the constraints on the modified gravity parameters whereas the lensing constraining power is negligible on the ΛCDM parameters. On the other hand the estimation is biased for all the parameters if lensing is not taken into account.
This effect is particularly significant for the modified gravity parameters. Our findings show the importance of including lensing in galaxy clustering analyses for testing General Relativity.


Ming Jiang PhD Defense

Event: Ming Jiang's Thesis Defence

Date: 10/11/2017

Venue: Salle Galilée, Bât: 713C (CEA-Saclay)

My thesis is approaching its final destination after 3 years of work! I am pleased to announce you that my defense will be held at 2 pm on November 10th in Galilée room. You are welcomed to my defense!

Multichannel Compressed Sensing and its Applications in Radioastronomy

The new generation of radio interferometer instruments, such as LOFAR and SKA, will allow us to build radio images with very high angular resolution and sensitivity. One of the major problems in interferometry imaging is that it involves an ill-posed inverse problem because only a few Fourier components (visibility points) can be acquired by a radio interferometer. Compressed Sensing (CS) theory is a paradigm to solve many underdetermined inverse problems and has shown its strength in radio astronomy.

This thesis focuses on the methodology of Multichannel Compressed Sensing data reconstruction and its application in radio astronomy. For instance, radio transients are an active research field in radio astronomy but their detection is a challenging problem because of low angular resolution and low signal-to-noise observations. To address this issue, we investigated the sparsity of temporal information of radio transients and proposed a spatial-temporal sparse reconstruction method to efficiently detect radio sources. Experiments have shown the strength of this sparse recovery method compared to the state-of-the-art methods.

A second application is concerned with multi-wavelength radio interferometry imaging in which the data are degraded differently in terms of wavelength due to the wavelength-dependent varying instrumental beam. Based on a source mixture model, a novel Deconvolution Blind Source Separation (DBSS) model is proposed. The DBSS problem is not only non-convex but also ill-conditioned due to convolution kernels. Our proposed DecGMCA method, which benefits from a sparsity prior and leverages an alternating projected least squares, is an efficient algorithm to tackle simultaneously the deconvolution and BSS problems. Experiments have shown that taking into account joint deconvolution and BSS gives much better results than applying sequential deconvolution and BSS.