Slides: BioComp Summer School 2017 on bio-inspired computing hardware

The good folks at the GdR BioComp (@GDRBioComp)organized what looked like an awesome BioComp Summer School 2017 that was to provide an introduction to subject areas around bio-inspired computing hardware. Here are the slides shown at the conference. Photos taken during the school are here.

Invited speakers and slides

• Chloé-Agathe Azencott, Mines ParisTech, France, Machine Learning and applications to genomics  

• Chloe-Agathe-Azencott-intro-ml 

• Chloe-Agathe-Azencott-Genomics

• Geoffrey W. Burr, IBM Almaden Research Center, USA, Memristive neuromorphic hardware 

• Geoffrey-Burr

• Elisabetta Chicca, Cognitive Interaction Technology Center of Excellence, Bielefeld Universit, Germany, Neuromorphic electronic circuits for building autonomous cognitive systems 

• Elisabetta-Chicca1

•  Elisabetta-Chicca2

• Steve Furber, School of Computer Science, University of Manchester, UK, SpiNNaker: ARM system-on-chip architecture  

• Steve-Furber1  

• Steve-Furber2

• Vincent Gripon, IMT Atlantique, Lab Sticc, France, Indexing, storing and retrieving data in neural networks  

• Vincent-Gripon1  

• Vincent-Gripon2

• Konstantin K. Likharev, Department of Physics and Astronomy, Stony Brook University, USA, Nanoelectronic/Superconductive Neuromorphic Networks  

• Konstantin-Likharev

• Jean-Pascal Pfister, University of Zurich and ETH, Switzerland, Learning and inference at the level of single synapses and spiking neurons  

• Jean-Pascal-Pfister1  

• Jean-Pascal-Pfister2  

• Jean-Pascal-Pfister-NP2016_Notes11  

• Jean-Pascal-Pfister-NP2016_Notes12

• Panayiota Poirazi, Computational Biology Laboratory, Heraklion, Greece, Computational neuroscience: the role of dendrites in learning and memory  

• YiotaPoirazi1  

• YiotaPoirazi2

• Damien Querlioz, C2N CNRS, France, Computing with unreliable nanodevices  

• Damien-Querlioz

• Gregor Schöner, Institut für Neuroinformatik, Germany, Cognition in embodied and situated nervous systems  

• GregorSchoner1 

• GregorSchoner2

• Rufin VanRullen, CerCo, France, Brain oscillations and perception  

• VanRullen-Biocomp-SummerSchool-2017

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Videos: DALI 2017 Symposium

Here are videos of the DALI 2017 - Symposium ( h/t B. Ravi.) - the site for the DALI meeting is here . Previously: videos of the two workshops of DALI 2017

• Data Efficient Reinforcement Learning  and 
• Theory of Generative Adversarial Networks

Probabilistic Deep Learning - Stock Take 2017 - Sebastian Nowozin by snwz 54:19

On Possible Relationships between Episodic Memory, Semantic Memory and Perception - Volker Tresp by snwz 43:33

Data Science in the Wolfram Language - Sebastian Bodenstein by snwz 57:24

Thank you to the organizers for video taping the meeting !

• Local Chair: Pablo M. Olmos, Universidad Carlos III de Madrid, 
• Isabel Valera, Max Planck Institute for Software Systems
• General Chair: Zoubin Ghaharamani, University of Cambridge, 
• Bernhard Schölkopf, MPI for Intelligent Systems Tübingen
• Symposium Chair: Neil Lawrence, Amazon and University of Sheffield
• Workshops Chair: Thomas Hofmann, ETH Zurich
• Organization Assistants Sabrina Nepozitek, ETH Zurich

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Videos: DALI 2017 - Workshop - Theory of Generative Adversarial Networks

I just came across the DALI 2017 Theory of Generative Adversarial Networks workshop videos of the talks that took place there thanks to B. Ravi.

DALI 2017 - Workshop - Theory of Generative Adversarial Networks - Introduction by snwz 11:08

Two-Sample Tests, Integral Probability Metrics, and GAN Objective - Dougal J. Sutherland

by snwz 30:33

Connecting GANs, Actor-Critic Methods and Multilevel Optimization - David Pfau

by snwz 32:07

Generative Density Estimation: Convexity and Boosting - Olivier Bousquet

by snwz 31:07

Generator-aware Discriminators & Discriminator-aware Generators - David Duvenaud by snwz 25:35

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks - Emily Denton by snwz 27:27

Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play - Arthur Szlam by snwz 32:35

On Generative Adversarial Networks and Density Estimation - Fernando Perez-Cruz by snwz

Thank you to the organizers for video taping the meeting !

• Local Chair: Pablo M. Olmos, Universidad Carlos III de Madrid, 
• Isabel Valera, Max Planck Institute for Software Systems
• General Chair: Zoubin Ghaharamani, University of Cambridge, 
• Bernhard Schölkopf, MPI for Intelligent Systems Tübingen
• Symposium Chair: Neil Lawrence, Amazon and University of Sheffield
• Workshops Chair: Thomas Hofmann, ETH Zurich
• Organization Assistants Sabrina Nepozitek, ETH Zurich

h/t Ravi.





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DALI 2017 - Workshop - Data Efficient Reinforcement Learning

Here are videos of the DALI 2017 Workshop on Data Efficient Reinforcement Learning ( h/t B. Ravi.)

Learning flexible models of nonlinear dynamical systems - Thomas Schön by snwz 29:34

Towards Safe Learning during Closed-loop Control - Melanie Zeilinger by snwz  30:43

Goal-Driven Dynamics Learning for Model-Based RL - Roberto Calandra by snwz 25:09

When the patient in front of you is the data source - Aldo Faisal by snwz 27:18

Unifying Multi-Step Reinforcement Learning Methods through Matrix Splittings - Pierre-Luc Bacon by snwz 23:38

Unsupervised Learning for RL - Max Jaderberg by snwz 39:13

Innate Knowledge - Chris Watkins by snwz 43:24

Thank you to the organizers for video taping the meeting !

• Local Chair: Pablo M. Olmos, Universidad Carlos III de Madrid, 
• Isabel Valera, Max Planck Institute for Software Systems
• General Chair: Zoubin Ghaharamani, University of Cambridge, 
• Bernhard Schölkopf, MPI for Intelligent Systems Tübingen
• Symposium Chair: Neil Lawrence, Amazon and University of Sheffield
• Workshops Chair: Thomas Hofmann, ETH Zurich
• Organization Assistants Sabrina Nepozitek, ETH Zurich

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Saturday Morning Video: Martin Arjovsky (WGAN) Interview on Alex Lamb's "The Nutty Netter" YouTube channel.

I just mentioned Siraj's YouTube videos as one of the new ways for the community to rapidily code some of the latest Machine Learning subjects. There is another way to get an insight on how things come about: Interviews.

Here is Alex Lamb's YouTube channel: The Nutty Netter where he interviews "Martin Arjovsky (NYU PhD student studying Machine Learning and AI) on the theory of generative adversarial networks, biological plausibility of deep credit assignment, and his work on the Wasserstein GAN."

Another video: Variational Inference and Deep Learning: An Intuitive Introduction
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Nuit Blanche in Review (June 2017)

Credits: NASA/JPL-Caltech/SwRI/MSSS/Jason Major

Since the last Nuit Blanche in Review (May 2017) we've had three implementations related to Deep Neural Networks, a few in-depth post ranging from training nets to compressive sensing,  a dataset, two Paris Machine Learning meetups, one meeting announcement, several videos of talks and four job announcements. Enjoy !

Implementations

• FreezeOut: Accelerate Training by Progressively Freezing Layers - implementation -
• Self-Normalizing Neural Networks - implementation -
• DiracNets: Training Very Deep Neural Networks Without Skip-Connections - implementation -

In depth

• Learning to Learn without Gradient Descent by Gradient Descent
• CATERPILLAR: Coarse Grain Reconfigurable Architecture for Accelerating the Training of Deep Neural Networks
• Learning Deep ResNet Blocks Sequentially using Boosting Theory
• Hyperparameter Optimization: A Spectral Approach
• Kronecker Recurrent Units
• Training Quantized Nets: A Deeper Understanding
• Coreset Construction via Randomized Matrix Multiplication
• Inexact Gradient Projection and Fast Data Driven Compressed Sensing
• Compressive Statistical Learning with Random Feature Moments
• Compressive optical interferometry
• Coherent inverse scattering via transmission matrices: Efficient phase retrieval algorithms and a public dataset - Dataset -

Book

• Book:Artificial Intelligence and Games by Georgios N. Yannakakis and Julian Togelius

Dataset

• Coherent inverse scattering via transmission matrices: Efficient phase retrieval algorithms and a public dataset - Dataset -

Paris Machine Learning meetup

• Paris Machine Learning #10 Ending Season 4 : Large-Scale Video Classification, Community Detection, Code Mining, Maps, Load Monitoring and Cognitive.
• Paris Machine Learning meetup "Hors Série" #13: Automatic Machine Learning

Meeting

• Meeting: Medical imaging in the era of AI, Yonsei University, Korea

slides

• Slides: Machine Learning Summer School @ Max Planck Institute for Intelligent Systems, Tübingen, Germany

Videos

• Saturday Morning Videos: The Changing Landscape of Education and The Awesome Siraj Raval
• Saturday Morning Videos: "Structured Regularization for High-Dimensional Data Analysis" @IHP Regularization Methods for Large Scale Machine Learning
• Videos: Structured Regularization for High-Dimensional Data Analysis: Submodular Functions,computing the Non-computable via sparsity, the SDP approach to graph clustering, the hidden clique problem, robust deconvolution
• Videos: Structured Regularization for High-Dimensional Data Analysis: Compressed Sensing: Structure and Imaging & Matrix and graph estimation
• Saturday Morning Videos: "Structured Regularization for High-Dimensional Data Analysis" Majorization-Minimization Subspace Algorithms for Large Scale Data Processing, Regularization Methods for Large Scale Machine Learning
• Saturday Morning Videos: Seminars of the Data Science Colloquium of the ENS: Beyond SGD, Functional brain mapping, What physics can tell us about inference?, Can Big Data cure Cancer?
• Sunday Morning Videos: NIPS 2016 workshop on nonconvex optimizations.
• Saturday Morning Video: Deep Learning Meets Sparse Coding, Chandra Sekhar Seelamantula

Job:

• Job; Postdoc Opening in Statistical Learning and Optimization, USC
• Job: Lecturer / Senior Lecturer in Machine Learning and Computational Intelligence, University of Surrey, UK
• CSjob: Postdoc, Optimisation for Matrix Factorisation, Toulouse, France
• CSJob: PhD position “Tensors for System Identification” at ELEC, Vrije Universiteit Brussel (VUB)

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Saturday Morning Videos: The Changing Landscape of Education and The Awesome Siraj Raval

Starting in 2013, one of the most important thing we noticed in the roster of the Paris Machine Learning meetup was a new breed of people young and less young who would get their education from Andrew Ng's coursera course*. That trend has accelerated over time and much of the close to 5800 members of the meetup are getting their education from non-traditional outlets. We sometimes  have stories of students teaching their engineering school professors. Some students even take time off from their studies to focus on getting an education through Kaggle competitions.
In this new education landscape, I recently noticed the awesome Siraj Raval, here is one of his recent YouTube video on second order approximation:

All of Siraj's videos are on his channel. What's interesting here is that subjects are not simple as the video above. Siraj often does some live coding and gets into recent subject of interest in the ML literature such as :

• Generative Adversarial Networks for Style Transfer (LIVE)
• Differentiable Neural Computer (LIVE)
• How to Generate Images with Tensorflow (LIVE)
• Generative Adversarial Networks (LIVE)

Siraj obviously has a GitHub repo.

* Andrew was kind enough to give a talk at the end of season 1 of the meetup. The room at Google was filled with 250 people it could accomodate. It was in large due to Andrew's status of having been a teacher to many in the room through the Coursera outlet. 

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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, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

Saturday Morning Videos: "Structured Regularization for High-Dimensional Data Analysis" @IHP Regularization Methods for Large Scale Machine Learning

The Summer School "Structured Regularization for High-Dimensional Data Analysis" was taking place at IHP Paris last week. Here are some of the videos of the fifth day with Lorenzo Rosasco

Structured Regularization Summer School - L. Rosasco - 3/4 - 22/06/2017

Structured Regularization Summer School - L. Rosasco - 4/4 - 22/06/2017

Previous videos are here: 

• Videos: Structured Regularization for High-Dimensional Data Analysis: Submodular Functions,computing the Non-computable via sparsity, the SDP approach to graph clustering, the hidden clique problem, robust deconvolution
• Videos: Structured Regularization for High-Dimensional Data Analysis: Compressed Sensing: Structure and Imaging & Matrix and graph estimation
• Saturday Morning Videos: "Structured Regularization for High-Dimensional Data Analysis" Majorization-Minimization Subspace Algorithms for Large Scale Data Processing, Regularization Methods for Large Scale Machine Learning

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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, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.