** 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:
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:
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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Other links:
Paris Machine Learning: Meetup.com||@Archives||LinkedIn||Facebook|| @ParisMLGroup< br/>
About LightOn: Newsletter ||@LightOnIO|| on LinkedIn || on CrunchBase || our Blog
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