Sunday, December 17, 2017

The Future of Random Projections : A mini-workshop, ( December 22 )

Because this period of the year is magic (see Sunday Morning Insight: And You, What Are You Waiting For ?), Florent and I decided to organize a mini-workshop on Random Projections on Friday not far from where the Curies made their discoveries. Here is the announcement. Please note that we are ok with remote presentations.



The Future of Random Projections : a mini-workshop
----------------------------------------------------------------------
Random Projections have proven useful in many areas ranging from signal processing to machine learning. In this informal mini-workshop, we aim to bring together researchers from different areas to discuss this exciting topic and its future use in the area of big data, heavy computations, and Deep Learning.  
Where and When:
  • Friday, December 22, 9:00am to 12:30pm (Paris time)

Confirmed Speakers:


If you want to join or to give a talk, please answer to the mail. Since this is a short notice, we can also accept skype contributions.

Florent Krzakala and Igor Carron
The schedule for the workshop will be on Nuit Blanche on Thursday.

Credit image: Rich Baraniuk


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Saturday, December 16, 2017

Saturday Morning Video: Petascale Deep Learning on a Single Chip Speaker: Tapabrata Ghosh, Vathys


Tapa Ghosh of Vathys.ai is presenting a new technology that aims at dealing with the one bottelneck few people in the hardware for AI are not focusing on: Data movement. Give this man his funding !
(Unrelated: At LightOn, we solve the data movement in a different fashion)




Vathys.ai is a deep learning startup that has been developing a new deep learning processor architecture with the goal of massively improved energy efficiency and performance. The architecture is also designed to be highly scalable, amenable to next generation DL models. Although deep learning processors appear to be the "hot topic" of the day in computer architecture, the majority (we argue all) of such designs incorrectly identify the bottleneck as computation and thus neglect the true culprits in inefficiency; data movement and miscellaneous control flow processor overheads. This talk will cover many of the architectural strategies that the Vathys processor uses to reduce data movement and improve efficiency. The talk will also cover some circuit level innovations and will include a quantitative and qualitative comparison to many DL processor designs, including the Google TPU, demonstrating numerical evidence for massive improvements compared to the TPU and other such processors. 

h/t Iacopo and Reddit 




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Friday, December 15, 2017

CfP: LVA/ICA 2018 14th International Conference on Latent Variable Analysis and Signal Separation July 2-6, 2018, University of Surrey, Guildford, UK http://cvssp.org/events/lva-ica-2018

Mark just sent me the following:

Dear Igor,
The forthcoming LVA/ICA 2018 international conference on Latent Variable Analysis and Signal Separation may be of interest to many Nuit Blanche readers, particularly those working on sparse coding or dictionary learning for source separation. The submission deadline is approaching! Please see below for the latest Call for Papers.
Best wishes, Mark
Here it is:

====================================
= LVA/ICA 2018 - CALL FOR PAPERS ==
14th International Conference on Latent Variable Analysis and Signal Separation
July 2-6, 2018
University of Surrey, Guildford, UK
Paper submission deadline: January 15, 2018
====================================

The International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018, is an interdisciplinary forum where researchers and practitioners can experience a broad range of exciting theories and applications involving signal processing, applied statistics, machine learning, linear and multilinear algebra, numerical analysis and optimization, and other areas targeting Latent Variable Analysis problems.
We are pleased to invite you to submit research papers to the 14th LVA/ICA which will be held at the University of Surrey, Guildford, UK, from the 2nd to the 6th of July, 2018. The conference is organized by the Centre for Vision, Speech and Signal Processing (CVSSP); and the Institute of Sound Recording (IoSR).
The proceedings will be published in Springer-Verlag's Lecture Notes in Computer Science (LNCS).

== Keynote Speakers ==
- Orly Alter
Scientific Computing & Imaging Institute and Huntsman Cancer Institute, University of Utah, USA
- Andrzej Cichocki
Brain Science Institute, RIKEN, Japan
- Tuomas Virtanen
Laboratory of Signal Processing
Tampere University of Technology, Finland

== Topics ==
Prospective authors are invited to submit original papers (8-10 pages in LNCS format) in areas related to latent variable analysis, independent component analysis and signal separation, including but not limited to:
- Theory:
* sparse coding, dictionary learning
* statistical and probabilistic modeling
* detection, estimation and performance criteria and bounds
* causality measures
* learning theory
* convex/nonconvex optimization tools
* sketching and censoring for large scale data
- Models:
* general linear or nonlinear models of signals and data
* discrete, continuous, flat, or hierarchical models
* multilinear models
* time-varying, instantaneous, convolutive, noiseless, noisy,
over-complete, or under-complete mixtures
* Low-rank models, graph models, online models
- Algorithms:
* estimation, separation, identification, detection, blind and
semi-blind methods, non-negative matrix factorization, tensor
decomposition, adaptive and recursive estimation
* feature selection
* time-frequency and wavelet based analysis
* complexity analysis
* Non-conventional signals (e.g. graph signals, quantum sources)
- Applications:
* speech and audio separation, recognition, dereverberation and
denoising
* auditory scene analysis
* image segmentation, separation, fusion, classification, texture
analysis
* biomedical signal analysis, imaging, genomic data analysis,
brain-computer interface
- Emerging related topics:
* sparse learning
* deep learning
* social networks
* data mining
* artificial intelligence
* objective and subjective performance evaluation

== Venue ==
LVA/ICA 2018 will be held at the University of Surrey, Guildford, in the South East of England, UK. The university is a ten minute walk away from the town centre, which offers a vibrant blend of entertainment, culture and history. Guildford is 40 minutes from London by train, and convenient for both London Heathrow and London Gatwick airports.

== Conference Chairs ==
- General Chairs:
Mark Plumbley - University of Surrey, UK
Russell Mason - University of Surrey, UK
- Program Chairs
Sharon Gannot - Bar-Ilan University, Israel
Yannick Deville - Université Paul Sabatier Toulouse 3, France

== Important Dates ==
- Paper submission deadline: January 15, 2018
- Notification of acceptance: March 19, 2018
- Camera ready submission: April 16, 2018
- Summer School: July 2, 2018
- Conference: July 3-6, 2018

== Website ==
For further information and information on how to submit, please visit:

We look forward to your participation,
The LVA/ICA 2018 Organizing Committee
===============================
--
Prof Mark D Plumbley
Professor of Signal Processing
Centre for Vision, Speech and Signal Processing (CVSSP)
University of Surrey, Guildford, Surrey, GU2 7XH, UK




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Thursday, December 14, 2017

CSjob: Multimedia / Research Scientist or Principal Research Scientist - Signal Processing, MERL, Massachusetts, USA

Petros just sent me the following:

Dear Igor, 
I hope you are doing well. We are excited to have a new opening in the Computational Sensing Team at MERL. I would appreciate it if you can post this on your blog, or otherwise disseminate as you see fit, and encourage anyone you think might be a good candidate to apply. Posting and application link is also here: http://www.merl.com/employment/employment.php#MM29
Thanks!
Petros
Sure Petros, here is the job ad:



MM29 - Multimedia / Research Scientist or Principal Research Scientist - Signal Processing
MERL's Computational Sensing Team is seeking an exceptional researcher in the area of signal processing, with particular emphasis on signal acquisition and active sensing technologies. Applicants are expected to hold a Ph.D. degree in Electrical Engineering, Computer Science, or a closely related field.
The successful candidate will have an extensive signal processing background and familiarity with related techniques, such as compressive sensing and convex optimization. Specific experience with wave propagation or PDE constrained inverse problems, or with signal acquisition via ultrasonic, radio, optical or other sensing or imaging modalities, is a plus. Applicants must have a strong publication record in any of these or related areas, demonstrating novel research achievements.
As a member of our team, the successful candidate will conduct original research that aims to advance state-of-the-art solutions in the field, with opportunities to work on both fundamental and application-motivated problems. Your work will involve initiating new projects with long-term research goals and leading research efforts.
MERL is one of the most academically-oriented industrial research labs in the world, and the ideal environment to thrive as a leader in signal processing. MERL strongly supports, encourages, and values academic activities such as publishing and presenting research results at top conferences, collaborating with university professors and students, organizing workshops and challenges, and generally maintaining an influential presence in the scientific community.







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Wednesday, December 13, 2017

Ce soir Paris Machine Learning Meetup #4 Season 5: K2, Datathon ICU, Scikit-Learn, Multimedia fusion, Private Machine Learning



So today is Paris Machine Learning Meetup #4, Season 5. Wow ! Thanks to Invivoo for sponsoring this meetup (food and drinks afterwards) and especially thanks for give us this awesome place!


The video streaming is here:


Capacity := +/- 170 seats / First-come-first-serve / then doors close

Schedule :

6:45PM doors open / 7-9:00PM talks / 9-10:00PM drinks/foods / 10:00PM end



Gael Varoquaux (INRIA), Some new and cool things in Scikit-Learn

An update on the scikit-learn project: new and ongoing features, code improvements, and ecosystem.

Nhi Tran (Invivoo), Multimedia fusion for information retrieval and classification

“Multimodal information fusion is a core part of various real-world multimedia applications. Image and text are two of the major modalities that are being fused and have been receiving special attention from the multimedia community. This talk focuses on the joint modelling of image and text by learning a common representation space for these two modalities. Such a joint space can be used to address the image/text retrieval and classification applications.”

Morten Dahl, Private Machine Learning

By mixing machine learning with cryptographic tools such as homomorphic encryption we may hope to for instance train model on sensitive data previously out of reach. Although still maturing, in this talk we will look at some of these techniques and how they were applied to a few concrete use cases.

Hardware: The War for AI supremacy, Recent developments, a short reviewIgor Carron, LightOn.io

This is a small review of the recent hardware development in Machine Learning/Deep Learning.

Tuesday, December 12, 2017

The Case for Learned Index Structures

Here is a different kind of The Great Convergence: when neural networks go after data structures, (hashes, etc....) and eventually database systems....




Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible.





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Monday, December 11, 2017

Compressive 3D ultrasound imaging using a single sensor

Pieter just sent me the following:

Dear Igor,
I have been following your blog for a couple of years now as it served as an excellent introduction to the field of CS and an active source of inspiration for new ideas. Many thanks for that! It was a quite a journey, but finally we managed to get some form of CS working in the field of ultrasound imaging. In our paper (online today:http://advances.sciencemag.org/content/3/12/e1701423, and a short video about this work: https://www.youtube.com/watch?v=whbbaF1nT4A ) we show that 3D ultrasound imaging can be done using only one sensor and a simple coding mask. Unfortunately we do not show any phase transition map and there is not much exploitation of sparsity but it does show that hardware prototyping and the utilisation of signal structure in conjunction with linear algebra can reveal powerful, new ways of imaging.
It would mean a lot to me (a long-held dream) if you could mention our paper on your blog some time.


Kind regards,
Pieter Kruizinga
Awesome Pieter !



Compressive 3D ultrasound imaging using a single sensor by Pieter Kruizinga, Pim van der Meulen, Andrejs Fedjajevs, Frits Mastik, Geert Springeling, Nico de Jong, Johannes G. Bosch and Geert Leus

Three-dimensional ultrasound is a powerful imaging technique, but it requires thousands of sensors and complex hardware. Very recently, the discovery of compressive sensing has shown that the signal structure can be exploited to reduce the burden posed by traditional sensing requirements. In this spirit, we have designed a simple ultrasound imaging device that can perform three-dimensional imaging using just a single ultrasound sensor. Our device makes a compressed measurement of the spatial ultrasound field using a plastic aperture mask placed in front of the ultrasound sensor. The aperture mask ensures that every pixel in the image is uniquely identifiable in the compressed measurement. We demonstrate that this device can successfully image two structured objects placed in water. The need for just one sensor instead of thousands paves the way for cheaper, faster, simpler, and smaller sensing devices and possible new clinical applications. 




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Monday, December 04, 2017

Nuit Blanche in Review (October and November 2017)

It's been two months since the last Nuit Blanche in Review (September 2017). We've had two Paris Machine Learning meetups, a two-day meeting of France is AI. Nuit Blanche featured two theses, a few job postings. While NIPS 2017 is about to start. I also recall last year's NIPS in Barcelona, where there was a sense that the community would move in on other areas besides computer vision. From some general takeaways from #NIPS2016
  • With the astounding success of Deep Learning algorithms, other communities of science have essentially yielded to these tools in a manner of two or three years. I felt that the main question at the meeting was: which field would be next ? Since the Machine Learning/Deep Learning community was able to elevate itself thanks to high quality datasets such as MNIST all the way to Imagenet, it is only fair to see where this is going with the release of a few datasets during the conference including the Universe from OpenAI. Control systems and simulators (forward problems in science) seem the next target.
Well, if you take a look at the few papers of this past two months mentioned here on Nuit Blanche, it looks like GANs and other methods have essentially made their way into the building of recovery solvers: i.e. algorithms dedicated to build images/data back from measurements. The recent interest in the development of Deep Learning for physics  makes it likely we will soon build better sensing hardware. 

Another interesting item to us at LightOn this past month is the realization that Biologically Inspired Random Projections is a thing. 

Enjoy the postings.


Implementation

In-depth
Hardware
Thesis
Meetup
Videos and slides:
CfP
Job:


credit: NASA / JPL / Ricardo Nunes

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