Wednesday, December 18, 2019

LightOn’s AI Research Workshop — FoRM #4: The Future of Random Matrices. Thursday, December 19th

** Nuit Blanche is now on Twitter: @NuitBlog **

Tomorrow we will feature LightOn’s 4th AI Research workshop on the Future of Random Matrices (FoRM). It starts at 2pm on Thursday, December 19th (That’s 2pm CET/Paris, 1pm GMT/UTC/London, 8am EST/NY-Montreal, 5am PST/California, 9pm UTC+8/ Shenzhen). We have an exciting and diverse line-up with talks on compressive learning, binarized neural networks, particle physics, and matrix factorization.

Feel free to join us, or to catch the event livestream — link to be available on this page on the day of the event.

Without further ado, here is the program:

  • 1:45pm — Welcome coffee and opening. A short introduction about LightOn, Igor Carron
  • 2:00pm — Compressive Learning with Random Projections, Ata Kaban
  • 2:45pm — Medical Applications of Low Precision Neuromorphic Systems, Bodgan Penkovsky
  • 3:30pm — Comparing Low Complexity Linear Transforms, Gavin Gray4:00pm — Coffee break and discussions
  • 4:20pm —LightOn’s OPU+Particle Physics, David Rousseau, Aishik Ghosh, Laurent Basara, Biswajit Biswas
  • 5:00pm — Accelerated Weighted (Nonnegative) Matrix Factorization with Random Projections, Matthieu Puigt
  • 5:45pm — Wrapping-up and beers on our rooftop

Talks and abstracts

Ata Kaban, University of Birmingham.
Compressive Learning with Random Projections
By direct analogy to compressive sensing, compressive learning has been originally coined to mean learning efficiently from random projections of high dimensional massive data sets that have a sparse representation. In this talk we discuss compressive learning without the sparse representation requirement, where instead we exploit the
natural structure of learning problems.

Bodgan Penkovsky, Paris-Sud University.
Medical Applications of Low Precision Neuromorphic Systems
The advent of deep learning has considerably accelerated machine learning development, but its development at the edge is limited by its high energy cost and memory requirement. With new memory technology available, emerging Binarized Neural Networks (BNNs) are promising to reduce the energy impact of the forthcoming machine learning hardware generation, enabling machine learning on the edge devices and avoiding data transfer over the network. In this talk we will discuss strategies to apply BNNs to biomedical signals such as electrocardiography and electroencephalography, without sacrificing accuracy and improving energy use. The ultimate goal of this research is to enable smart autonomous healthcare devices.

Gavin Gray, Edinburgh University.
Comparing Low Complexity Linear Transforms
In response to the development of recent efficient dense layers, this talk discusses replacing linear components in pointwise convolutions with structured linear decompositions for substantial gains in the efficiency/accuracy tradeoff. Pointwise convolutions are fully connected layers and are thus prepared for replacement by structured transforms. Networks using such layers are able to learn the same tasks as those using standard convolutions, and provide Pareto-optimal benefits in efficiency/accuracy, both in terms of computation (mult-adds) and parameter count (and hence memory).

David RousseauAishik GhoshLaurent Basara, Biswajit Biswas. LAL Orsay, LRI Orsay, BITS University.
OPU+Particle Physics

LightOn’s OPU is opening a new machine learning paradigm. Two use cases have been selected to investigate the potentiality of OPU for particle physics:
  • End-to-End learning: high energy proton collision at the Large Hadron Collider have been simulated, each collision being recorded as an image representing the energy flux in the detector. Two classes of events have been simulated: signal are created by a hypothetical supersymmetric particle, and background by known processes. The task is to train a classifier to separate the signal from the background. Several techniques using the OPU will be presented, compared with more classical particle physics approaches.
  • Tracking: high energy proton collisions at the LHC yield billions of records with typically 100,000 3D points corresponding to the trajectory of 10,000 particles. Various investigations of the potential of the OPU to digest this high dimensional data will be reported.

Matthieu Puigt, Université du Littoral Côte d’Opale.
Accelerated Weighted (Nonnegative) Matrix Factorization with Random Projections
Random projections belong to the major techniques used to process big data. They have been successfully applied to, e.g., (Nonnegative) Matrix Factorization ((N)MF). However, missing entries in the matrix to factorize (or more generally weights which model the confidence in the entries of the data matrix) prevent their use. In this talk, I will present the framework that we recently proposed to solve this issue, i.e., to apply random projections to weighted (N)MF. We experimentally show the proposed framework to significantly speed-up state-of-the-art weighted NMF methods under some mild conditions.

The workshop will take place at IPGG, 6 Rue Jean Calvin, 75005 Paris. The location is close to both the Place Monge and the Censier-Daubenton subway stations on line7. it is also close to the Luxembourg station on the RER B line. The location is close to bus stops on the 21, 24, 27, 47, and 89 routes. Note that strikes are still ongoing, and some of these options may not be available.

We will be in the main amphitheater, downstairs on your right when you enter the building. Please register in advance on our meetup group so as to help us in the organization of the workshop.

Follow @NuitBlog or join the CompressiveSensing Reddit, the Facebook page, the Compressive Sensing group on LinkedIn  or the Advanced Matrix Factorization group on LinkedIn

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Wednesday, December 11, 2019

Ce Soir: Paris Machine Learning Meetup #2 Season 7: Symbolic maths, Data Generation thru GAN, "Prevision Retards" @SNCF, Retail and AI, Leveraging GPUs

** Nuit Blanche is now on Twitter: @NuitBlog **

A big thank you to Scaleway for hosting us in their inspiring office and sponsoring the networking event afterwards.

So this is quite exciting. Our meetup group has 7 999 members and we are going to organize a meetup in a town that is paralyzed by strikes. During the course of existence of this meetup, we have seen worse.  

For those of you who will not be able to make it, all information slides and link to streaming are below:

Tabular data are the most common within companies. Generating synthetic data that respects the statistical properties of the original data can have several applications: a machine learning that respects data privacy, improving the robustness of a model in relation to data drift, etc. Since 2018, there has been an increasing number of academic publications presenting the use of GANs on this type of data, particularly on patient medical data. We have performed a proof of concept on real data, and present the results of several models from the research, namely the Wasserstein GAN, the Wasserstein GAN with Gradient Penalty and the Cramér-GAN, with the objective of "model compatibility", i.e. the possibility of using synthetic data to replace real data to train a classifier.

2. Eloïse Nonne, Soumaya Ihihi, "Prévisions Retards" a Machine Learning project led by e.SNCF's Data IoT team.
Its goal is to integrate predictions of train delays into the SNCF mobile application. Every day, our model predicts delays for the next 7 days, at each stop, for every train in Paris area network. The challenge of this project is to improve the reliability of passenger information and to provide more relevant routes for the application users. We will present the project, from the definition of needs and exploratory data analysis, to its industrialization in the cloud and the reliability of its predictions.

This talk is focussed on AI and ML applications in retail. Discover how Carrefour is transforming through the introduction of the Google - Carrefour Lab by Elina Ashkinazi-Ildis, Director of the Lab. Then go further with the "shelf out detection" usecase presented by Kasra Mansouri, Data Scientist within Artefact.

RAPIDS makes it possible to have end-to-end data science pipelines run entirely on GPU architecture. It capitalizes on the parallelization capabilities of GPUs to accelerate data preprocessing pipelines, with a pandas-like dataframe syntax. GPU-optimized versions of scikit-learn algorithms are available, and RAPIDS also integrates with major deep learning frameworks.
This talk will present RAPIDS and its capabilities, and how to integrate it in your pipelines.

Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be **surprisingly good** at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations. We propose a syntax for representing mathematical problems, and methods for generating large datasets that can be used to train sequence-to-sequence models. We achieve results that outperform commercial Computer Algebra Systems such as Matlab or Mathematica.

Follow @NuitBlog or join the CompressiveSensing Reddit, the Facebook page, the Compressive Sensing group on LinkedIn  or the Advanced Matrix Factorization group on LinkedIn

Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email.

Other links:
Paris Machine||@Archives||LinkedIn||Facebook|| @ParisMLGroup
About LightOnNewsletter ||@LightOnIO|| on LinkedIn || on CrunchBase || our Blog
About myselfLightOn || Google Scholar || LinkedIn ||@IgorCarron ||Homepage||ArXiv