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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