Nuit Blanche in Review (July-August 2019)

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

Landing in Oxia Planum, NASA/JPL/University of Arizona

Since the last Nuit Blanche in Review (June 2019), we've have a few in-depth material, two posts about LightOn, a hardware implementation, some conferences, courses and some job announcements. Enjoy !

In-depth:

• Transfer Learning as a Tool for Reducing Simulation Bias: Application to Inertial Confinement Fusion
• Enhanced Seismic Imaging with Predictive Neural Networks for Geophysics
• Asymmetric Random Projections

LightOn

• LightOn’s Summer Blog Post Series:  Faith No Moore and A New Hope
• Two new preprints using LightOn Optical Processing Unit (OPU)

CS Hardware:

• Hardware realization of a CS-based MIMO radar

Conferences:

• #ISIT2019 International Symposium on Information Theory
• SampTA 2019 Abstracts/Papers.
• SPARS 2019, HDPA-2019 and papers from the Theoretical Physics for Deep Learning ICML 2019 Workshop

Courses:

• Courses: Marc Lelarge's hands-on tour to deep learning with PyTorch
• PRAIRIE AI Summer School, Paris, October 3-5th 2019

Jobs:

• Job: Several postdocs, Ground Breaking Deep Learning Technology for Monitoring the Brain during Surgery with Commercialization Opportunity, University of Pittsburgh
• Jobs: PhD scholarship on Algorithms for Event-Driven Camera Analysis at Western Sydney University, Australia
• Jobs: 2 PhD and RA positions at University of Luxembourg

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About myself: LightOn || Google Scholar || LinkedIn ||@IgorCarron ||Homepage||ArXiv

Nuit Blanche in Review (June 2019)

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

Credit: JAXA / JMA / Seán Doran, 

Planet Earth Music by Schubert/Budapest Strings, Seán Doran, 

Much has happened since the last Nuit Blanche in Review (May 2019). In particular, we featured a number of architecture search implementation approaches and had our last general Paris Machine Learning meeting of season 6 as well as several in-depth videos. Enjoy !

Implementations:

• Improving Neural Architecture Search Image Classifiers via Ensemble Learning 
• NAS-Bench-101: Towards Reproducible Neural Architecture Search 
• Meta-learning of textual representations 
• An Open Source AutoML Benchmark 
• Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings 
• Improving Automated Variational Inference with Normalizing Flows 
• Efficient Forward Architecture Search 
• Bayesian Optimization over Sets
• Graduated Optimisation of Black-Box Functions
• Accelerating the Nelder - Mead Method with Predictive Parallel Evaluation
• Toward Instance-aware Neural Architecture Search
• A simple dynamic bandit algorithm for hyper-parameter tuning
• Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks 
• Random Search and Reproducibility for Neural Architecture Search
• Deep Learning based compressive sensing - Highly Technical Reference Page/Aggregator, implementation -
• Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching 

In-depth:

• One-Shot Neural Architecture Search via Compressive Sensing
• Degrees of Freedom Analysis of Unrolled Neural Networks
• Distributed Learning with Random Features
• Inverse Scattering via Transmission Matrices: Broadband Illumination and Fast Phase Retrieval Algorithms
• Random Projections for Quadratic Programs over a Euclidean Ball
• The low-rank eigenvalue problem

Videos:

• Saturday Morning Videos: AutoML Workshop at ICML 2019
• Saturday Morning Videos: L4DC: Learning for Dynamics and Control, May 30 & 31, 2019, MIT
• Fast.ai courses: Part 2: Deep Learning from the Foundations
• Ce soir: Paris Machine Learning Meetup, Season 6 Finale. Data4Good, Hybridation, Sequence Predictions, Unreliable Data

Meetings:

• CfP: The 2019 Conference on Mathematical Theory of Deep Neural Networks (DeepMath 2019) Princeton Club, New York City, Oct 31-Nov 1 2019.

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Other links:
Paris Machine Learning: Meetup.com||@Archives||LinkedIn||Facebook|| @ParisMLGroup< br/> About LightOn: Newsletter ||@LightOnIO|| on LinkedIn || on CrunchBase || our Blog
About myself: LightOn || Google Scholar || LinkedIn ||@IgorCarron ||Homepage||ArXiv

Nuit Blanche in Review (May 2019)

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

Since the last Nuit Blanche in Review (July 2018 - April 2019 we had a slew of videos, implementations, hardware of interest and the continuing sense that many of the techniques used in Deep Learning/ Differentiable Programming are taking over the traditional ways of solving things: from compressive sensing to advanced matrix factorization. The Great Convergence is about how these tools are learned from data as opposed to using concept of sparsity/structured signal.  Eventually, it is going to be how we will design data driven hardware....

In other news, LightOn changed its website/logo. To reflect our dual nature hardware/ML, we now use LightOn.ai as well as LightOn.io !

Implementations

• "Generative models are the new sparsity": The spiked matrix model with generative priors - implementation -
• Sparse-Plex library and a fast OMP implementation
• Unfolded ISTA and Orthogonality Regularization -implementation -
• Image Completion Using Low Rank Tensor Method - implementation -
• Deep Compressed Sensing - implementation -
• A New Theory for Sketching in Linear Regression -implementation-
• One-shot distributed ridge regression in high dimensions - implementation-
• Compressed Sensing with Deep Image Prior and Learned Regularization - implementation -

In-depth

• Tree Search Network for Sparse Regression
• SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
• Learning step sizes for unfolded sparse coding
• Differentially Private Compressive k-Means
• Estimating the inverse trace using random forests on graphs
• Uncertainty Quantification for high-dimensional inverse problems
• On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training
• A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices
• Quantifying entanglement in a 68-billion dimensional quantum system
• Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections
• Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation
• GAN-based Projector for Faster Recovery in Compressed Sensing with Convergence Guarantees

Hardware/experimental

• M87 reconstruction by the Event Horizon Telescope project
• Compressive Single-pixel Fourier Transform Imaging using Structured Illumination
• Compressive optical interferometry
• Ghost imaging with the human eye

LightOn:

• LightOn Workshop #3: The Future of Random Matrices (Friday May 24th, 2019)

Paris Meetups:

• Calendrier des événements Machine Learning à Paris (in French)
• Ce soir, Paris Machine Learning Meetup: Fraud & Bank attacks, Robust Learning, Data Olympics Paris Berlin 2019

Videos:

• Saturday Morning Videos: Deep Learning Boot Camp, May 28 – May 31, 2019, Simons Institute for the Theory of Computing.
• Saturday Morning Videos: Imaging and Machine Learning Workshop, @IHP Paris, April 1st – 5th , 2019
• Saturday Morning Videos: IPAM Workshop IV: Deep Geometric Learning of Big Data and Applications (May 20 - 24, 2019)
• Saturday Morning Video: Learning Representations Using Causal Invariance, Léon Bottou
• Saturday Morning Videos: Workshop on the Interface of Machine Learning and Statistical Inference, BANFF
• Saturday Morning Video: ISSCC 2019, Deep Learning Hardware: Past, Present, and Future, Yann LeCun
• Saturday Morning Videos: Workshop III: Geometry of Big Data, Geometry and Learning from Data in 3D and Beyond, IPAM
• Saturday Morning Video: New Approaches to Image and Video Reconstruction Using Deep Learning (Jeremy Howard, Jason Antic, and Uri Manor)
• Saturday Morning Videos: Andreas C. Müller's COMS W4995 Applied Machine Learning, Columbia/NYU CDS, Spring 2019

Jobs:

• Jobs: Several Postdocs in machine learning and information processing, LIONS, EPFL, Switzerland

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Nuit Blanche in Review (July 2018 - April 2019)

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

The last Nuit Blanche in Review was back on July 9th, 2018 and much has happened as regards to LightOn as we raised $3M+ in December and are looking forward to make our technology more available to the Machine Learning community. Coming to this place, Nuit Blanche is back !

I think one of the most interesting theoretical development has the ability to study nonlinearities in Deep Learning thanks in part to the work of Jeffrey Pennington as featured here before. Some of the papers that cite his work are featured in this review and bring much freshness to the field. In other news, Nuit Blanche is also on Twitter at this address @NuitBlog. In the meantime, here is what happened for the past few months here in this 66th Nuit Blanche in Review, enjoy !

Implementations

• Manopt 5.0, toolbox release: Optimization on Manifolds: - implementation -
• SPORCO: Convolutional Dictionary Learning - implementation -
• Rank Minimization for Snapshot Compressive Imaging - implementation -

In-depth:

• Data-Driven Design for Fourier Ptychographic Microscopy
• Quantifying entanglement in a 68-billion dimensional quantum system
• Neumann Networks for Inverse Problems in Imaging
• Beyond Thunderspline: Mad Max: Affine Spline Insights into Deep Learning/ From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference
• Phase Retrieval Under a Generative Prior
• Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections
• Why are Big Data Matrices Approximately Low Rank?
• Eigenvalue distribution of nonlinear models of random matrices
• Enhanced Expressive Power and Fast Training of Neural Networks by Random Projections
• A Neural Architecture for Bayesian CompressiveSensing over the Simplex via Laplace Techniques

LightOn

• LightOn: Forward We Go !
• Shedding Light on the “Grand Débat”

Hardware: 

• CSHardware: Development of sparse coding and reconstruction subsystems for astronomical imaging, João Rino-Silvestre
• Ghost imaging with the human eye

CfPs:

• CfP: Khipu 2019: Latin American Meeting in Artificial Intelligence, Montevideo, Uruguay, November 11-15th, 2019
• CfP: 2019 Conference on Mathematical Theory of Deep Neural Networks (DeepMath 2019)
• CfP: Call for Papers: Special Issue on Information Theory Applications in Signal Processing

References: 

• Book: High-Dimensional Probability An Introduction with Applications in Data Science by Roman Vershynin
• Highly Technical Reference Page: The Rice University Compressive Sensing page.
• #ICML2018 Tutorial: Toward theoretical understanding of deep learning, Sanjeev Arora

Videos: 

• Saturday Morning Videos: Statistical Modeling for Shapes and Imaging, 11 March 2019 - 15 March 2019, IHP
• Videos: New Deep Learning Techniques, February 5 - 9, 2018, IPAM Workshop
• Videos: Sublinear Algorithms and Nearest-Neighbor Search workshop, Nov. 27 – Nov. 30, 2018 (Simon Institute and Kavli Foundation)
• Video: Robust and High-Dimensional Statistics workshop - Oct. 29 – Nov. 2, 2018, (Simon Institute and Kavli Foundation)
• Video: Randomized Numerical Linear Algebra and Applications Workshop - Sep. 24 – Sep. 27, 2018, (Simon Institute and Kavli Foundation)
• Videos: Foundations of Data Science Boot Camp workshop, Aug. 27 – Aug. 31, 2018 (Simon Institute and Kavli Foundation)
• Videos: Variational Methods and Optimization in Imaging, at IHP Paris, February 4th – 8th , 2019
• Ce soir Paris Machine Learning #6 Season 6: Adversarial Attacks, ML in Production, XAI, Photo Editing
• Ce soir, Paris Machine Learning #5 season 6: Explainable AI, Unity Challenge, Ethical AI
• The 100th Paris Machine Learning meetup tonight: La revanche des neurones, DL on Healthrecords, Search-Oriented Conversational AI, Nanotechnology and electricity consumption
• Paris ML E#2 S#6: Conscience, Code Analysis, Can a machine learn like a child?
• Paris Machine Learning #1 S6 at Vente-Privée
• Sunday Morning Video (in french): Les travaux de Grothendieck.sur les espaces de Banach, Gilles. Pisier (Lectures grothendieckiennes)
• WiMLDS and Paris Machine Learning meetup Hors série #1: Scalable Automatic Machine Learning with H2O with Erin Ledell
• “And we’re back for Season 6” Paris Machine Learning Newsletter, September 2018 (in French)

Jobs:

• Job: Postdoctoral Researcher in Small Data Deep Learning and Explainable Machine Learning, Livermore, CA
• CSJob: PhD and Postdoc positions KU Leuven: Optimization frameworks for deep kernel machines

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About myself: LightOn || Google Scholar || LinkedIn ||@IgorCarron ||Homepage||ArXiv

Nuit Blanche in Review (February - June 2018)

It's been five months, already, here is what we featured on Nuit Blanche since the last Nuit Blanche in Review (Janvier 2018). During that time, Mila Nikolova left us. Here are some other things that happened.

Implementations

• Fast Convex Pruning of Deep Neural Networks - implementation -
• Unsupervised Learning with Stein's Unbiased Risk Estimator - implementation -
• Deep Mesh Projectors for Inverse Problems - implementation -
• McKernel: A Library for Approximate Kernel Expansions in Log-linear Time - implementation -
• NEWMA: a new method for scalable model-free online change-point detection - implementation -
• Video, Preprint and Implementation: Measuring the Intrinsic Dimension of Objective Landscapes
• Efficient Neural Architecture Search via Parameter Sharing - implementation -

LightOn 

• The Future of Random Projections II
• Mini-workshop: The Future of Random Projections II, 1pm-4pm, May 2nd, 2018, Paris, France
• Sunday Morning Insight: LightOn Cloud: Light Based Technology for AI on the Cloud
• Processeurs optiques et traitement de données de grande dimension/ Optical Co-Processors and High Dimensional Data Processing, Paris, April 5, 2018

Meetings:

• CfP: OIP'21 : Optics for information processing in the 21th century, 23-26 May 2018 Florence (Italy)
• Sparse Representations and Compressed Sensing Workshop, March 23rd 2018, Inria Paris
• Institute for Advanced Study - Princeton University Joint Symposium on "The Mathematical Theory of Deep Neural Networks", Tuesday March 20th

Posters:

• Posters: SysML 2018 Conference

In-depth

• Quantized Compressive K-Means
• Stabilizing Embedology: Geometry-Preserving Delay-Coordinate Maps
• q-Neurons: Neuron Activations based on Stochastic Jackson's Derivative Operators
• Adversarial Noise Layer: Regularize Neural Network By Adding Noise / Training robust models using Random Projection (implementation)
• Revisiting Skip-Gram Negative Sampling Model With Regularization
• Blocked Direct Feedback Alignment: Exploring the Benefits of Direct Feedback Alignment
• SUNLayer: Stable denoising with generative networks
• Gradients explode - Deep Networks are shallow - ResNet explained
• Random projections in gravitational wave searches of compact binaries
• Randomness in Deconvolutional Networks for Visual Representation
• Economic Impact of Compressive Sensing

Book:

• Book: Dictionary Learning Algorithms and Applications

Video:

• Video, Preprint and Implementation: Measuring the Intrinsic Dimension of Objective Landscapes
• Videos: Computational Theories of the Brain, Simons Institute for the Theory of Computing
• Videos: The "Institute for Advanced Study - Princeton University Joint Symposium on 'The Mathematical Theory of Deep Neural Networks'"
• Saturday Morning Videos: Bandit Convex Optimization, PGMO Lecture 1 and 2
• Saturday Morning Video:On Characterizing the Capacity of Neural Networks using Algebraic Topology by William Guss
• Saturday Morning Videos: NIPS2017 Meta Learning Symposium videos
• Saturday Morning Videos: IPAM Workshop on New Deep Learning Techniques

job:

• PhD and Postdoc positions KU Leuven (ERC Advanced grant E-DUALITY)
• CSjob: Research Fellow in Machine Listening, University of Surrey, UK

Around the blogs:

• Around the blogs in 78 hours (and 7 million page views on Nuit Blanche )

conferences

• Artificial Intelligence Summer School, Grenoble, 2-6 July 2018

Paris Machine Learning meetups:

• Paris Machine Learning #9, Season 5: Adaptive Learning, Emotion Recognition, Search, AI and High Energy Physics, How Kids code AI?
• Paris Machine Learning #9: "Past & Present & Future of Deep Learning & Artificial Intelligence" Juergen Schmidhuber
• IMPAC IMaging-PsychiAtry Challenge: predicting autism A data challenge on Autism Spectrum Disorder detection
• Ce soir/Today: Paris Machine Learning Meetup Hors Série #4 Saison 5: Le Canada et l'IA
• Paris Machine Learning #8 Season 5, Chat with Self Driving Cars Engineers at Voyage, Funding AI at Zeroth, NVIDIA GTC, Quality inspection at Scortex and Low Rank Matrices
• Paris Machine Learning Meetup Newsletter, Avril 2018: “Be the change you wish to see in the world” [In French]
• Paris Machine Learning Meetup #7 Season 5, Natural Language Understanding (NLU), AI for HR, decentralized AI
• Paris Machine Learning Meetup #6 Season 5: Nano-neurons, Drug design, Electronic Health Records...
• IMPAC IMaging-PsychiAtry Challenge: predicting autism A data challenge on Autism Spectrum Disorder detection

IA en France

• L'intelligence Artificielle au service de l'humain / AI for Humanity

Other

• Video: Falcon Heavy Test Flight (and my non-story with SpaceX)

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Nuit Blanche in Review (Janvier 2018)

Since the last Nuit Blanche in Review (December 2017), I wrote A Short Tale of Computation and AI but Nuit Blanche also featured two implementations, several in-depth papers, several videos, meetups and jobs. Enjoy !

Implementations

• Deep Complex Networks - implementation -
• Deep Image Prior - implementation -

In-depth

• Intriguing Properties of Randomly Weighted Networks: Generalizing while Learning Next to Nothing
• Size-Independent Sample Complexity of Neural Networks
• On Random Weights for Texture Generation in One Layer Neural Networks
• Understanding Deep Representations through Random Weights
• Towards Understanding the Invertibility of Convolutional Neural Networks
• Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering
• The stochastic interpolation method. The layered Structure of Tensor Estimation and Phase Transitions, Optimal Errors and Optimality of Message-Passing in GLMs
• Random Incident Sound Waves for Fast Compressed Pulse-Echo Ultrasound Imaging

Videos:

• Saturday Morning Videos: Theories of Deep Learning: videos and slides
• Video and Slides: Understanding and Improving Deep Learning With Random Matrix Theory, Jeffrey Pennington

Meetup

• Ce soir: Paris Machine Learning meetup Hors Série 1 (Season 5) Self-Supervised Imitation, Pierre Sermanet
• OpenMined Hackathon in Paris (Saturday, January 13th)
• Ce soir: Paris Machine Learning Meetup #5 Season 5: IronCar, Mobile AI, Rules mining
• Video Streaming: State of Artificial Intelligence for Environmental Science Applications panel at AMS conference in Austin TX

CfP

• Call for participants: Workshop and Advanced school "Statistical physics and machine learning back together" in Cargese, Corsica, France, August 20-31, 2018

Jobs:

• Job: Machine Learning and Computer Vision Engineer, Scortex, Paris, France
• Job: Research engineer position in the DREAM project at ISIR, Sorbonne-Université, Paris, France.
• CSjob: Postdoc for research in reservoir computing, University of St.Gallen, Switzerland
• CSjob: One PhD and one Postdoc Position, Low-Rank Matrix Approximations
• CSJob: PhD Position for Signal Processing for Energy Harvesting Sensor Networks, Uppsala University, Swedens
• Job: Ingénieur R&D Machine learning/Deep learning, INA, France
• Job: Several Tenured and Tenure-Track Faculty positions in robotics at ENSTA ParisTech, France

Other

• Saturday Morning Video: The Overview Effect

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Nuit Blanche in Review (December 2017)

Happy New Year 2018 to all !

Since the last Nuit Blanche in Review (October and November 2017), we at LightOn had our First Light ! (more details later on the upcoming LightOn's newsletter) and started having a more general conversation on Random Projections through the organization of a small workshop ( Program / Streaming: The Future of Random Projections : a mini-workshop on December 22nd. 

This past month we featured a few papers, had a Paris Machine Learning meetup, some videos and a few jobs. Enjoy !

• Lectures on Randomized Numerical Linear Algebra by Petros Drineas and Michael Mahoney
• The Case for Learned Index Structures
• Compressive 3D ultrasound imaging using a single sensor

Paris Machine Learning meetup

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

Videos:

• #NIPS2017 Video: "Back When We Were Young', Ali Rahimi, Ben Recht, Test of Time award for the Random Kitchen Sinks papers
• #NIPS2017 Video: "Why AI Will Make it Possible to Reprogram the Human Genome", Brendan Frey
• Saturday Morning Videos: Machine Learning Summer School, Max Planck Institute for Intelligent Systems, Tübingen, Germany
• Saturday Morning Video: Petascale Deep Learning on a Single Chip Speaker: Tapabrata Ghosh, Vathys

CfP

• CfP: LVA/ICA 2018 14th International Conference on Latent Variable Analysis and Signal Separation July 2-6, 2018, University of Surrey, Guildford, UK 

Jobs:

• Job: Two Postdocs: Large-scale, storage-optimal continuous optimization / Convex relaxations for discrete optimization problems, EPFL, Switzerland
• Job: Internship position (Spring/Summer 2018 + PhD position follow-up), IFPEN, France
• CSjob: Multimedia / Research Scientist or Principal Research Scientist - Signal Processing, MERL, Massachusetts, USA

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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

• One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models - implementation -
• Learned D-AMP: Principled Neural Network based Compressive Image Recovery - implementation -

In-depth

• Globally and Locally Consistent Image Completion
• Deep Generative Adversarial Networks for Compressed Sensing Automates MRI - implementation - / Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery
• Optimizing Kernel Machines using Deep Learning / Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations
• ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
• Phase Transitions, Optimal Errors and Optimality of Message-Passing in Generalized Linear Models
• Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks
• DCASE 2017 TASK 1: Acoustic Scene Classification Using Shift-Invariant Kernels and Random Features
• Linear Dimensionality Reduction in Linear Time: Johnson-Lindenstrauss-type Guarantees for Random Subspace / Structure-aware error bounds for linear classification with the zero-one loss
• Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks

Hardware

• DiffuserCam: Lensless Single-exposure 3D Imaging

Thesis

• Thesis: Sketching for Large-Scale Learning of Mixture Models by Nicolas Keriven
• Thesis: Efficient Methods and Hardware for Deep Learning by Song Han

Meetup

• France is AI, Program for Day One
• Ce soir: Paris Machine Learning #3 Season 5, PokémonGO, Unsupervised ML in high dimension, Prevision.io, Learning to program
• Tonight: Paris Machine Learning Meetup #2 Season 5: Reinforcement Learning for Malware detection, Fraud detection, video scene detection and Climate Change

Videos and slides:

• Slides and Videos: Deep RL Bootcamp 26-27 August 2017, Berkeley CA
• Slides: Université Cote d'Azur/UCA Deep Learning School, May 2017

CfP

• CfP: Workshop on Approximating high dimensional functions, Alan Turing Institute, London,18-19 December 2017.

Job:

• CSJob: 2 Postdocs, Computer Vision and Machine Learning for Robotics / Navigation, decision making and teleoperation of autonomous vehicles, INRIA, France
• Job: Faculty position, ECE, Ohio State
• CSJob: Four Postdoctoral Research Assistants, Mathematical Institute and the Oxford-Emirates Data Science Lab (OEDSL)
• Job: Harvard University Data Science Initiative Postdoctoral Fellows Program
• Job: Post-doctoral fellow/PhD position in statistical genomics and imaging analysis, Tulane University, LA
• Jobs: Two Postdocs, MIT Institute for Foundations of Data Science (MIFODS)

credit: NASA / JPL / Ricardo Nunes

TVASHTAR PATERAE on Io FROM GALILEO ORBIT I27

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Nuit Blanche in Review (September 2017)

Much has happened since the last Nuit Blanche in Review (August 2017). This past month, we saw  The Demise of Cassini and the Rise of Artificial InteIligence but also an implementation, a hardware implementation, several in-depth preprints, an overview, several videos and a job announcement. Enjoy !

Implementations

• Stabilizing GAN Training with Multiple Random Projections - implementation -

Hardware:

• CSHardware: InView Multi-Pix Camera Demonstrates 1FPS SWIR Imaging

In-depth:

• Deep Learning and Inverse Problems
• On Principal Components Regression, Random Projections, and Column Subsampling
• Lazy stochastic principal component analysis / Near Optimal Sketching of Low-Rank Tensor Regression
• Second-Order Optimization for Non-Convex Machine Learning, FLAG, GIANT and Non-Convex Optimization Under Inexact Hessian
• Deep Null Space, Deep Factorization and the Last Image of Cassini
• Random Subspace with Trees for Feature Selection Under Memory Constraints / Learning Mixture of Gaussians with Streaming Data
• Random Subspace with Trees for Feature Selection Under Memory Constraints / Learning Mixture of Gaussians with Streaming Data
• Modeling random projection for tensor objects
• Super-Resolution Imaging Through Scattering Medium Based on Parallel Compressed Sensing / Cell Detection with Deep Convolutional Neural Network and Compressed Sensing / Exploit imaging through opaque wall via deep learning

Overviews

• Overviews: Deep Learning on Reinforcement Learning, Music Generation and Recommender Systems

Meetings/Meetups/CfP

• Paris Machine Learning #1 Season 5: Code Mining, Mangas, Drug Discovery, Open Law, RAMP
• NIPS 2017 accepted papers
• CfP: "Tensor Image Processing" in Signal Processing: Image Communcation, 2018.

Videos:

• Videos: Montreal AI Symposium
• Saturday Morning Videos: Cognitive Computational Neuroscience (CCN) 2017

Jobs:

• CSjob: Assistant Professor, High Dimensional Data Analysis, University of Colorado at Boulder

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Nuit Blanche in Review (August 2017)

NASA’s Lunar Reconnaissance Orbiter shows the shadow of the Moon cast on the United States during the Aug. 21, 2017, total solar eclipse.

Credits: NASA/GSFC/Arizona State University

Many things have happened in the last month since the last Nuit Blanche in Review (July 2017) including an Eclipse and Harvey. We also have had several implementations made available by their respective authors, a thesis, a sruvey, more in-depth articles, some interesting videos, some job openings... Enjoy !

Implementations

• SMASH: One-Shot Model Architecture Search through HyperNetworks - implementation -
• 30 new Numerical Tours in Julia - implementation -
• Solving ill-posed inverse problems using iterative deep neural networks / Jobs: 2 Postdocs @ KTH, Sweden - implementation -
• SPORCO: A Python package for standard and convolutional sparse representations - implementation 
• Convexified Convolutional Neural Networks - implementation -
• Meta Networks - implementation -

Thesis

• Thesis: Towards Scalable Gradient-Based Hyperparameter Optimizitation in Deep Neural Networks by Jie Fu

Survey

• A Brief Survey of Deep Reinforcement Learning

In-depth

• ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections
• Natural Language Processing with Small Feed-Forward Networks
• On the Expressive Power of Deep Neural Networks
• A Closer Look at Memorization in Deep Networks
• Towards a Deeper Understanding of Training Quantized Neural Networks / BitNet: Bit-Regularized Deep Neural Networks / Deep Binary Reconstruction for Cross-modal Hashing
• Learning Transferable Architectures for Scalable Image Recognition / The Cost of Everything and the Value of Nothing
• Randomization or Condensation?: Linear-Cost Matrix Sketching Via Cascaded Compression Sampling / A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication / Effective sketching methods for value function approximation
• Sketched Subspace Clustering
• When is Network Lasso Accurate?, The Network Nullspace Property for Compressed Sensing of Big Data over Networks, Semi-Supervised Learning via Sparse Label Propagation
• Randomized apertures: high resolution imaging in far field

Conferences:

• Papers: ICML2017 workshop on Implicit Models
• CfP: 17th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences, American Meteorological Society

Videos:

• Saturday Morning Videos: Bridging Continuous and Discrete Optimization Boot Camp, Simons Institute at Berkeley, August 21-25, 2017
• Sunday Morning Videos: Andrew Ng's "Heroes of Deep Learning"
• Saturday Morning Videos: Convolutional Neural Networks for Visual Recognition (Spring 2017 Stanford CS 231n)

Job:

• Job: Smart Grid Data Scientist, Neuchâtel, Switzerland
• Solving ill-posed inverse problems using iterative deep neural networks / Jobs: 2 Postdocs @ KTH, Sweden - implementation -

Other:

• The Sun Eclipse of 2017

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