Paris Machine Learning #1 S6 at Vente-Privée

So after two "Hors Série" meetups, we decided to start Season 6 of the Paris Machine Learning meetup tonight. We'll talk about algorithms used at Vente Privée, one of the most ambitious company in France but also about Quantum computing and Machine Learning and eventually how SNCF is becoming algorithm/data driven. The streaming and the presentations will be accessible below:

Nous aurons donc le premier meetup régulier de la saison chez Vente-privée. C'est un cadre unique comme nous l'avons vu il y a deux ans. Il y aura surement une visite organisée des locaux.

Un grand merci à Vente Privée, de nous accueillir !

AGENDA :

Doors open 6:45PM // talk 7-9PM // network 9-10:30PM

Program

Jéremie Jakubowicz, Vente Privee, Data Science at Vente Privee

In this talk we will reveal what's been happening within the Data Science Team at Vente Privee this year...

Betty Moreschini, Personnalisation and recommendation systems at vente-privee

At vente-privee we customize a lot of things, and this talk would describe the mechanism behind catalog customization. When a customer enters a sales, we create a section filled with items recommended for this specific customer, based on its previous purchases, and other criteria.

Nicolas Gaude, prevision.io, Quantum automated machine learning

Quantum computing paradigm applied to automated machine learning. an efficient alternative to hyperparamter search.

Heloise Nonne, SNCF, La vie du rail : que fait-on à la SNCF en data science ?

Le rôle de la Fab Big Data et de l'équipe data science et engineering pour le groupe SNCF.. Quelques projets en cours représentatifs :

• Adhérence : mieux connaître, localiser et comprendre les phénomènes de perte d'adhérence (c'est le contact entre la roue et le rail).
• Energie : analyse des consommations d'energie électrique et prévision de consommation.
• Projets prospectifs :
• Active learning
• Lisibilité des modèles de ML

Be my App, Présentation des 2 événements Machine Learning a venir: Intel AI DevCamp & AI For a Better World avec Umanis/Google Cloud

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Job: Postdoctoral Researcher in Small Data Deep Learning and Explainable Machine Learning, Livermore, CA

Bhavya just sen me the following:

Hi Igor, 

I would like to ask you a favor. We are looking for a Postdoctoral Researcher interested in small data deep learning and explainable machine learning. I was wondering whether it is possible to list the opening on your blog. Information on the available position is below.
We are looking for a Postdoctoral Researcher with expertise in statistics, machine learning, convex/non-convex optimization and/or uncertainty quantification. Postdoctoral Researcher will support ongoing efforts concerned with small-data deep learning and related topics, such as, transfer learning, generative modeling, self-supervised or unsupervised learning, and explainable ML. This position is in the Computation Directorate within the Center for Applied Scientific Computing (CASC) Division at Lawrence Livermore National Lab, Livermore, CA.
Essential Duties

• Research, design, implement and apply a variety of advanced data science methods in multiple application areas (such as material science, high energy physics, predictive medicine, cybersecurity) in a collaborative scientific environment.

• Document research by publishing papers at conferences/journals such as NIPS, ICML, ICLR, IJCAI, AAAI, AISTATS, ACL, CVPR, JMLR or similar.

Qualifications

• Ph.D. in statistics or computer science or a related field.

• Experience in modern machine learning environments (TensorFlow, PyTorch, etc.).

• Proficiency in one or more of the following machine learning areas: deep learning, reinforcement learning, and Bayesian nonparametric.

• Knowledge of C/C++, Python.

If interested, please contact me directly at kailkhura1@llnl.gov.
Regards,
Bhavya

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A Neural Architecture for Bayesian CompressiveSensing over the Simplex via Laplace Techniques

Steffen just sent me the following:

Dear Igor,

I'm a long-time reader of your blog and wanted to share our recent paper on a relation between compressed sensing and neural network architectures. The paper introduces a new network construction based on the Laplace transform that results in activations such as ReLU and gating/threshold functions. It would be great if you could distribute the link on nuit-blanche. 

The paper/preprint is here:
https://ieeexplore.ieee.org/document/8478823
https://www.netit.tu-berlin.de/fileadmin/fg314/limmer/LimSta18.pdf 

Many thanks and best regards,
Steffen 

Dipl.-Ing. Univ. Steffen Limmer
Raum HFT-TA 412
Technische Universität Berlin
Institut für Telekommunikationssysteme
Fachgebiet Netzwerk-Informationstheorie
Einsteinufer 25, 10587 Berlin

Thanks Steffen ! Here is the paper:

A Neural Architecture for Bayesian CompressiveSensing over the Simplex via Laplace Techniques by Steffen Limmer and Sławomir Stanczak.

This paper presents a theoretical and conceptual framework to design neural architectures for Bayesian compressive sensing of simplex-constrained sparse stochastic vectors. First we recast the problem of MMSE estimation (w.r.t. a pre-defined uniform input distribution over the simplex) as the problem of computing the centroid of a polytope that is equal to the intersection of the simplex and an affine subspace determined by compressive measurements. Then we use multidimensional Laplace techniques to obtain a closed-form solution to this computation problem, and we show how to map this solution to a neural architecture comprising threshold functions, rectified linear (ReLU) and rectified polynomial (ReP) activation functions. In the proposed architecture, the number of layers is equal to the number of measurements which allows for faster solutions in the low-measurement regime when compared to the integration by domain decomposition or Monte-Carlo approximation. We also show by simulation that the proposed solution is robust to small model mismatches; furthermore, the proposed architecture yields superior approximations with less parameters when compared to a standard ReLU architecture in a supervised learning setting.

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Sunday Morning Video (in french): Les travaux de Grothendieck.sur les espaces de Banach, Gilles. Pisier (Lectures grothendieckiennes)

This video in French mentions the connection between Grothendieck's work and some of the subject areas mentioned on Nuit Blanche.( see here, here and here).

La thèse de Grothendieck et son article ultérieur intitulé "Résumé de la théorie métrique des produits tensoriels topologiques" (1956) a eu un énorme impact sur le développement de la géométrie des espaces de Banach pendant les 60 dernières années. Nous passerons en revue ce "Résumé" en nous concentrant sur le résultat que Grothendieck lui-même a appelé le théorème fondamental de la théorie métrique des produits tensoriels, maintenant devenu "l'inégalité de Grothendieck" ou "le théorème de Grothendieck". Ce résultat a récemment fait une apparition pour le moins inattendue dans plusieurs domaines a priori fort éloignés des préoccupations de Grothendieck. L'une a trait aux C ∗ -algèbres et aux espaces d'opérateurs (ou "espaces de Banach non-commutatifs"), une autre aux inégalités de Bell et à leur "violation" en mécanique quantique, une dernière relie la constante de Grothendieck au problème P=NP et à la théorie des graphes.

Here is a review that covers some of what is mentioned in the video: 

Grothendieck's Theorem, past and present by Gilles Pisier

Probably the most famous of Grothendieck's contributions to Banach space theory is the result that he himself described as "the fundamental theorem in the metric theory of tensor products". That is now commonly referred to as "Grothendieck's theorem" (GT in short), or sometimes as "Grothendieck's inequality". This had a major impact first in Banach space theory (roughly after 1968), then, later on, in C∗-algebra theory, (roughly after 1978). More recently, in this millennium, a new version of GT has been successfully developed in the framework of "operator spaces" or non-commutative Banach spaces. In addition, GT independently surfaced in several quite unrelated fields:\ in connection with Bell's inequality in quantum mechanics, in graph theory where the Grothendieck constant of a graph has been introduced and in computer science where the Grothendieck inequality is invoked to replace certain NP hard problems by others that can be treated by "semidefinite programming" and hence solved in polynomial time. In this expository paper, we present a review of all these topics, starting from the original GT. We concentrate on the more recent developments and merely outline those of the first Banach space period since detailed accounts of that are already available, for instance the author's 1986 CBMS notes.

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