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Thursday, February 11, 2016

De nouveau ce soir, Paris Machine Learning Meetup #7 Season 3: Neural Networks for Predictive Maintenance, Machine Learning in Quantitative Finance, Introduction to scikit-learn



This second meetup for February (videos and slides of yesterday's meetup are here ) will be hosted and sponsored by Quantmetry at Village by CA. The video streaming is visible on the video above. Here is the program and attendant slides:

Nous menons actuellement un projet d'expérimentation sur la maintenance prédictive de matériel roulant dans le domaine du ferroviaire. Plusieurs phases de preuve de concept afin d'identifier les données utiles ont été menées. Nous présenterons une étude prospective avec plusieurs approches utilisant des réseaux de neurones pour détecter les défaillances du matériel : méthodologie, résultats préliminaires et perspectives.
• Delaney Granizo-Mackenzie, Quantopian, "Machine Learning in Quantitative Finance"

We will cover how machine learning techniques might fit into quantitative finance. This includes techniques to rank assets and construct spread based portfolios, and which types of machine learning applications don't work.
Scikit-learn is a popular Open Source library for Machine Learning in Python. This presentation will introduce the project and give demo how to use it in conjunction with other tools from the PyData ecosystem such as NumPy, pandas and Jupyter notebook. Finally we will review recent and ongoing developments if time allows.



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Wednesday, February 10, 2016

Ce soir: Paris Machine Learning Meetup #6 Season 3; ASTEC #NecMergitur, Beauty and danger of matrix completion, E-commerce and DL, Topic Modeling on Twitter streams and Cross-Lingual Systems

This February, we will have four meetups (on the 10th, 11th, 17th and 22nd)! Maybe it's a sign of times or maybe it's because there are 29 days this month, who knows. Let us note that we now have 3200 members and more than 1000 members on our LinkedIn group. Today, we will have the first of two meetups this week.



 

We will be hosted by Maltem Consulting Group who are also sponsoring the networking event afterwards. For this meetup we will have the following presentation (slides will be linked here before the actual meetup)

One short presentation of a project presented at the #Necmergitur:hackaton:

* Pitch; Manga Zossou, Projet AZTEC : Audio sensors for threat detection/système de capteurs audio pour détecter des menaces) at 11 minutres in the video. (in French)
and then:

* Franck Bardol and Igor Carron, introduction.

• Julie Josse, Beauty and danger of matrix completion methods: unveiling a black box's subtleties for better decision at 1h14 in the video. (in French)

• Andrei Yigal Lopatenko, Head of Search Quality @ WalmartLabs,  (remote from SF) What problems of ecommerce can deep learning solve? at 52 minutes in the video. (in English)
A short overview of ecommerce problem which can be solved with deep learning method with a tech dive into image similarity as a product recommendation problem. 

• Alex Perrier, (remote from Boston) at 24 minutes in the video. (in French)
Topic modeling avec LSA, LDA et STM appliqué aux streams de followers Twitter.
Topic modeling: LSA, LDA, STM avec code en python et R, Text mining, comment determiner le nombre de topics, comment visualiser les topics.

• Jean-Marc Marty, Proxem, The Quest for Cross-Lingual Systems  at 1h57 minutes in the video. (in French)
At Proxem, our clients ask us to extract information from e-mails, social medias, press articles, and basically any type of text you can imagine. In the standard case, the text to process is written in various languages. To establish systems that support a wide scale of languages and formats is one of the mission of our Research team.
I will focus during this talk on a paper that we've presented at EMNLP 2015 called Trans-Gram: Fast Cross-lingual Word Embeddings. The objective of this paper is to introduce a model that learns aligned word embeddings throughout a significant number of languages in a scalable way.
 
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Tuesday, February 09, 2016

Two-day workshop on "Computational and statistical trade-offs in learning", IHES, France, March 22-23

Francis just let me know of this two-day workshop on "Computational and statistical trade-offs in learning" which will take place at IHES on March 22-23, 2016

Computational and statistical trade-offs in learning

March 22-23, 2016

Institut des hautes etudes scientifiques, Centre de conference Marilyn et James Simons, 35 route de Chartres 91440 Bures sur Yvette

This workshop focuses on the computational and statistical trade-offs arising in various domains (optimization, statistical/machine learning). This is a challenging question since it amounts to optimize the performance under limited computational resources, which is crucial in the large-scale data context. One main goal is to identify important ideas independently developed in some communities that could benefit the others.

Invited speakers:

  • Pierre Alquier (ENSAE, Paris-Saclay)
  • Alexandre d'Aspremont (D.I., CNRS / ENS Paris)
  • Quentin Berthet (DPMMS, Cambridge Univ., UK)
  • Alain Celisse (Université de Lille)
  • Rémi Gribonval (INRIA, Rennes)
  • Emilie Kaufmann (CNRS, Lille)
  • Vianney Perchet (CREST, ENSAE Paris-Saclay)
  • Garvesh Raskutti (Wisconsin Institute for Discovery, Madison, USA)
  • Ohad Shamir (Weizmann Insitute, Rehovot, Israel)
  • Silvia Villa (Istituto Italiano di Tecnologia, Genova & MIT, Cambridge, USA)

    The conference is free and open to all, but registration is mandatory before March, 19. Please fill-in the form at

    https://indico.math.cnrs.fr/event/1007/

    where you will also find detailed information about the conference.


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    Job: Lectureship in Computer Vision/Image Analysis for Medicine & Healthcare , CVSSP, University of Surrey, UK

    Mark just sent the following: 


    Dear Igor, The following Lecturer job (similar to Assistant Professor level) may be of interest to Nuit Blanche readers, especially those interested in Medicine & Healthcare applications of vision/image analysis. Best wishes, Mark

    -----

    Lectureship in Computer Vision/Image Analysis for Medicine & Healthcare

    Centre for Vision, Speech and Signal Processing, University of Surrey, UK

    Closing Date: Thursday 18 February 2016

    http://jobs.surrey.ac.uk/003116

    The University offers a unique opportunity for an outstanding research leader to join the Centre for Vision, Speech and Signal Processing (CVSSP).

    The successful candidate is expected to build a research project portfolio to complement existing CVSSP strengths. The centre seeks to appoint an individual with an excellent research track-record and international profile to lead future growth of research activities in one or more of the following areas:

    *       Medical Image Analysis
    *       Image and Sensor Analysis for Healthcare
    *       Big Data Understanding for Healthcare
    *       Machine Learning & Pattern Recognition
    *       Machine Intelligence

    We now seek a strong research leader who can develop the existing activities of CVSSP and exploit the synergetic possibilities that exist within the centre, across the University and regionally with UK industry. You will possess proven management and leadership qualities, demonstrating achievements in scholarship and research at a national and international level, and will have substantial experience of teaching within HE.

    CVSSP is one of the primary centres for computer vision & audio-visual signal processing in Europe with over 120 researchers, a grant portfolio of £18M and a track-record of pioneering research leading to technology transfer in collaboration with UK industry.  Related to this post CVSSP, in collaboration with the Surrey Centres for Clinical & Sleep Research, has recently been awarded £1.2M equipment funding to support research in sensor networks to monitor & measure people for healthcare in the community.  CVSSP forms part of the Department of Electronic Engineering, recognised as a top department for both Teaching and Research. Further details of CVSSP: surrey.ac.uk/cvssp

    Closing date for applications: 18th February 2016

    For an informal discussion, please contact Professor Adrian Hilton, Director of CVSSP (a.hilton@surrey.ac.uk).

    --
    Prof Mark D Plumbley
    Professor of Signal Processing
    Centre for Vision, Speech and Signal Processing (CVSSP)
    University of Surrey
    Guildford, Surrey, GU2 7XH, UK
    Email: m.plumbley@surrey.ac.uk


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    Compressive Spectral Clustering



    Compressive Spectral Clustering by Nicolas Tremblay, Gilles Puy, Remi Gribonval, Pierre Vandergheynst

    Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object, and run k-means on these features to separate objects into k classes. Each of these three steps becomes computationally intensive for large N and/or k. We propose to speed up the last two steps based on recent results in the emerging field of graph signal processing: graph filtering of random signals, and random sampling of bandlimited graph signals. We prove that our method, with a gain in computation time that can reach several orders of magnitude, is in fact an approximation of spectral clustering, for which we are able to control the error. We test the performance of our method on artificial and real-world network data.
     
     
     
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    Monday, February 08, 2016

    ICLR 2016: List of accepted papers:

      
    From Hugo's twitter feed:
    Here is the list of accepted papers:



    Release Date: February 4, 2016
    Keywords: MVIC, Pluto, Ralph
    Credit: NASA/Johns Hopkins University Applied Physics Laboratory/Southwest Research Institute


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    Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly Detection

    Another use of Random Features: Streaming Anomaly Detection

    Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly Detection by Markus Schneider, Wolfgang Ertel, Fabio Ramos

    We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (EXPoSE), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with EXPoSE can be done in linear time and online (incremental) learning takes constant time per instance and model update. Furthermore, EXPoSE can make predictions in constant time, while it requires only constant memory. In addition we propose different methodologies for concept drift adaptation on evolving data streams. On several real datasets we demonstrate that our approach can compete with state of the art algorithms for anomaly detection while being significant faster than techniques with the same discriminant power.

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    Friday, February 05, 2016

    Compressed imaging by sparse random convolution

    Diego just sent me the following:

    Hello,

    I've followed NuitBlanche for a couple years now and it would be a honour for me if you would consider a recent publication of ours to appear in your CS hardware list.

    We only point out that Gaussian measurement matrices are not feasible when measuring incoherent light and propose to use sparse non-negative random measurement matrices instead. The article can be found here.

    Thanks a lot for giving us your time through your blog!

    Diego Marcos
    Sure Diego !


    Compressed imaging by sparse random convolution by Diego Marcos, Theo Lasser, Antonio López, and Aurélien Bourquard
    The theory of compressed sensing (CS) shows that signals can be acquired at sub-Nyquist rates if they are sufficiently sparse or compressible. Since many images bear this property, several acquisition models have been proposed for optical CS. An interesting approach is random convolution (RC). In contrast with single-pixel CS approaches, RC allows for the parallel capture of visual information on a sensor array as in conventional imaging approaches. Unfortunately, the RC strategy is difficult to implement as is in practical settings due to important contrast-to-noise-ratio (CNR) limitations. In this paper, we introduce a modified RC model circumventing such difficulties by considering measurement matrices involving sparse non-negative entries. We then implement this model based on a slightly modified microscopy setup using incoherent light. Our experiments demonstrate the suitability of this approach for dealing with distinct CS scenarii, including 1-bit CS.
     
     
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    Bachelor Thesis: Probabilistic Data-Driven Models for the Pushing Problem by Maria Bauz à Villalonga

    Using Random Features for the pushing problem:
     
     
     
    Probabilistic Data-Driven Models for the Pushing Problem by Maria Bauz à Villalonga


    Pushing actions are common mechanisms present in most human and industry manipulations. Nevertheless, finding a precise description for the motion of pushed objects is still an open problem. In this work, we will develop the first data-driven models that can describe the pushing motion taking into account its uncertainty. We will also explain how we collected a high-quality data set for pushing using real experiments that will be available online to motivate research in the pushing domain. A key challenge to describe pushing is understanding friction properly. In most situations, friction makes systems stochastic and introduces uncertainty in our predictions. Moreover, in robot applications, sensors can also add noise into our observations making our state-estimations uncertain. In consequence, our work will consider probabilistic algorithms such as Gaussian Processes to introduce for the first time the uncertainty of our system into the modeling of pushing. In this thesis, we also investigate how these models behave for the particular case of a square object being pushed in a single contact point. This is a good starting point for future generalizations of our models and has already allowed us to simulate properly the motion of pushed objects and validate or refute most typical assumptions considered when trying to describe the pushing problem theoretically. 
     
     
     
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    Thursday, February 04, 2016

    Paris Machine Learning Meetup Newsletter, Février 2016 (in French): Les murs et les moulins à vent.

     


    Paris Machine Learning Meetup Newsletter
    Février 2016

    0 L’édito de Franck et Igor
    1 Un Nouveau meetup : Paris Data Workshop (prochaine date 11 Février en journée)
    2 Quatre meetups du Paris Machine Learning en Février (10, 11, 17 et 22 Février le soir )
    3 Data Ninja (9 février en journée)
    4 Perspectives and New Challenges in Data Science (3 Février en journée)
    5 Cours de Yann Lecun au Collège de France (4 Février en journée et plusieures dates)
    6 Quantopian Course (10 Février pendant la journée)
    7 Deep Learning Meetup #5, (25 Février le soir)
    8 CDS RAMP on macroeconomic modelling and machine learning, (9 et 10 Févrieren journée)
    9 Bordeaux Machine Learning Meetup (10 Février le soir)
    10 Aix Marseille Machine Learning Meetup



    0 L’édito de Franck et Igor

    “When the wind changes, some build walls, others build windmills” Chinese proverb.

    “Quand les vents changent, certains construisent des murs, d’autres des moulins à vent”, proverbe Chinois.

    Nous aurons quatre meetups en Février. Pourquoi ? parce que les vents changent et il nous faut savoir ou mettre les moulins à vent.

    Nous avons donc maintenant plus de 3150 membres dans le groupe du meetup et plus de 1000 membres dans le groupe LinkedIn, ce qui montre que les algorithmes deviennent un sujet de société de plus en plus important. Beaucoup de choses se sont passés depuis le premier meetup de l’année 2016. Nous allons en prendre trois qui nous semblent indicatives de certaines tendances.

    Au meetup de décembre, nous préssentions l’intérêt de Netflix (une entreprise capitalisée a plus de 38 Mds de dollars) pour l’utilisation du Machine Learning dans l’analyse et peut-être même la création à Hollywood. Par un grand hasard, Netflix vient juste d’ouvrir un poste pour un content scientist. On se pose la question de savoir quand nous verrons des start-ups qui utiliseront les collections de films Français ou Européens pour créer de nouveaux contenus culturels intéressants au lieu d’être dans la crispation de protection de l’exception culturelle. Tout est une histoire de construire des moulins à vents.

    La transparence des algorithmes de prédiction devient une question souvent posée. Ainsi, nos députés veulent rendre publics les algorithmes des administrations. C’est intéressant mais ce qui nous semblent encore plus important c’est de pouvoir déterminer si ces algorithmes font le travail que l’on pense qu’ils doivent faire. A cet effet, nous organisons un meetup où l’on traitera de ces questions des “fair algorithms” c’est à dire des algorithmes non discriminants.  Voici une émission d’Aljazeera sur le sujet avec Suresh qui était un remote speaker l’année dernière mais qui sera à Paris pour le meetup du 17 février. La encore, peut-être que la remise à plat de certains algorithmes pourront enlever certains biais de notre société. Let take down these walls.

    Après Google, Microsoft vient de mettre en libre service son framework de Deep Learning: CNTK. C’est la 70ème bibliothèque de Deep Learning open source. Tout le monde se pose la question de l’endroit ou mettre ces moulins à vent.

    Bref, pour répondre à certaines de ces questions et d’autres, nous aurons donc  les présentateurs suivants:

    Avec Franck, nous instituons pour tout les meetups un “first-come first-in”. Nous ouvrirons les portes vers 18h50. Le hashtag du meetup est #MLParis. Nous ne pouvons avoir une bonne programmation que si nous n’avons pas à nous professionnaliser pour l’organisation des ces meetups. Jouez le jeu, en changeant votre RSVP. Ainsi vous envoyez le signal que vous faites attention aux autres. Merci d’avance.

    Si vous avez des informations à faire passer à la communauté, vous pouvez le faire en postant directement sur notre page Facebook, notre page Google+, ou sur notre groupe LinkedIn (n'oubliez pas de mettre [JOB] si c’est une annonce). Vous pouvez aussi nous contacter sur notre  compte Twitter: @ParisMLgroup, nous ferons un RT.


    Voila c’est tout pour ce mois-ci.




    1 Un Nouveau meetup : Paris Data Workshop (Franck)

    On a fait un tour d’horizon du Machine Learning le 28 Janvier au Master Class Machine Learning Episode 1. Il y avait 60 participants pour cette première session. La prochaine date est le 11 février 2016 et pour cela j’organise un nouveau meetup: Le Paris Data Workshop

    Un nouveau meetup afin de se rencontrer pour expérimenter et apprendre la Data-Science.
    Pas de maths, pas de formules compliquées ! A la place, les compétences et la compréhension afin de résoudre des cas concrets. Au programme, plusieurs Workshops (exposé 1h + exercice de Data-Science à résoudre ensemble 1h) et Master Class (exposé 1h) prévus

    Les débutants sont les bienvenus !

    Inscription, programme et calendrier ici : www.meetup.com/Paris-Data-Workshop

    A noter, certains ateliers seront payants (50 € / session) afin de rétribuer le formateur et la logistique. Si on trouve un sponsor, les ateliers deviendront alors gratuits !

    2  Quatre meetups du Paris Machine Learning en Février (Franck et Igor)

    Nous aurons quatre meetups ce mois de Février. C’est une première pour nous. Nous allons ouvrir les inscriptions dès demain pour les deux premiers meetups mais nous ne mettrons pas de liste d’attente. Toutes les présentations seront en ligne et un streaming sera organisé.

    Mercredi 10 Février, 2016 à 19h00. Pour ce meetup, nous sommes reçus par Maltem Consulting Group, le buffet est sponsorisé par Maltem Consulting Group.

    • Zossou Manga, Projet ASTEC : système de capteurs audio pour détecter des menaces, #NecMergitur
    • Julie Josse, Beauty and danger of matrix completion methods: unveiling a black box's subtleties for better decision
    • Andrei Yigal Lopatenko, Head of Search Quality @ WalmartLabs, (remote from SF) What problems of ecommerce can deep learning solve?
    • Alex Perrier, (remote from Boston) Topic modeling avec LSA, LDA et STM appliqué aux streams de followers Twitter.
    • Jean-Marc Marty, Proxem, The Quest for Cross-Lingual Systems

    Jeudi 11 Février, 2016 à 19h00. Pour ce meetup, nous sommes reçus par Quantmetry au Village by CA, le buffet est sponsorisé par Quantopian


    Mercredi 17 Février, 2016 à 19h00. Pour ce meetup, nous sommes reçus par DojoCrea, le buffet n’est pas encore sponsorisé (contactez-nous si vous êtes intéressé par le sponsoring du buffet). En clair, pas de sponsor, pas de buffet !


    Lundi 22 Février, 2016 à 19h00. Pour ce meetup, nous sommes reçus par Mobiskill , le buffet est sponsorisé par Mobiskill.

    • Bob Sturm, " Your machine learnings may not be learning what you think they are learning: Lessons in music and experimental design"

    3 Data Ninja (Franck)

    La session de février démarre le 9 février.
    Le programme : L’essentiel de la data-science en 9 sessions.

    Ouverture prochaine des billets sur meetup Paris Data Workshop

    4 Perspectives and New Challenges in Data Science

    L'Ecole des Ponts - ParisTech organise le Mercredi 3 février 2016 un colloque intitulé
    “Perspectives and new challenges in Data Science”

    Orateurs:
    François Bancilhon, PDG de Data Publica
    François Yvon, Groupe Traitement du Language Parlé, LIMSI
    Pierre-Paul Vidal, Directeur du groupe COGnition et Action, Université Paris 5
    Yannig Goude, EDF R&D, dpt Osiris
    Cyrille Dubarry, Criteo
    Vivien Mallet, équipe Clime, INRIA
    Alexandre Gramfort, CNRS-LTCI, Télécom-ParisTech
    Josef Sivic, équipe Willow, INRIA

    Pour plus d'informations sur le programme
    et l'inscription (gratuite mais obligatoire) voir

    5 Cours de Yann Lecun au Collège de France

    Yann Lecun est le directeur de la recherche en Intelligence Artificielle de Facebook. C’est le co-découvreur des réseaux profonds (Deep Learning). Il fera une série de cours au Collège de France à partir du 4 Février. Pour plus d’infos c’est ici sur le site du College de France. Normalement la majorité des interventions sont filmés (mais pas en streaming).

    6 Quantopian Course

    Dan Dunn de Quantopian nous informe d’un cours le 10 Février pendant la journée.

    We would like to invite you to attend our day-long, hands-on, algorithmic trading workshop happening on the 10th of February in Paris.

    The Workshop: An Introduction to Algorithmic Trading
    The Workshop will give you a strong foundation to craft a trading strategy. The curriculum has been vetted and used to teach by professors at top tier schools including: Harvard, Stanford, and Cornell.

    The workshop will:
    • Give you the ability to create and backtest your own basic trading strategies.
    • Show you how to use algorithmic trading tools that will help you craft your strategies.
    • Teach you how to correct for some statistical biases that can disrupt analysis.
    Attendees should have a beginner's knowledge of Python and college level math experience.

    Reserve Your Spot Today
    The ticket price for the Workshop is €625.00. Meals and snacks will be provided. If you would like to attend, please RSVP here.

    We hope you can join us!

    Dan Dunn

    7. Deep Learning Meetup #5

    Nos amis d’Heuritech organisent le Deep Learning Meetup #5 le jeudi 25 Février 2016.

    8 CDS RAMP on macroeconomic modelling and machine learning, (9 et 10 Février)

    Balazs Kegl organise un nouveau RAMP

    Next CDS RAMP on macroeconomic modelling and machine learning, Feb 9-10

    We have scheduled the next CDS RAMP (learn a surrogate model for an agent-based macroeconomic model), and Training Sprint (to learn the tools and the background). Preliminary information is here:


    Please sign up here if you'd like to attend the events:


    If it's you first RAMP, please don't forget to fill out the form at


    9 Bordeaux Machine Learning Meetup

    Le premier meetup du Bordeaux Machine Learning sera le 10 Février, Deep learning distribué depuis votre laptop

    10 Aix Marseille Machine Learning Meetup

    Il y a déja eu un meetup d’Aix Marseille Machine Learning Meetup. Si vous êtes dans la région abonnez-vous !





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