DataScience4you2me

**  Nuit Blanche is now on Twitter: @NuitBlog  ** NAS-Bench-101: Towards Reproducible Neural Architecture Search by  Chris Ying , Aaron Klein , Esteban Real , Eric Christiansen , Kevin Murphy , Frank Hutter Recent advances in neural architecture search (NAS) demand tremendous computational resources, which makes it difficult to reproduce experiments and imposes a barrier-to-entry to researchers without access to large-scale computation. We aim to ameliorate these problems by introducing NAS-Bench-101, the first public architecture dataset for NAS research. To build NAS-Bench-101, we carefully constructed a compact, yet expressive, search space, exploiting graph isomorphisms to identify 423k unique convolutional architectures. We trained and evaluated all of these architectures multiple times on CIFAR-10 and compiled the results into a large dataset of over 5 million trained models. This allows researchers to evaluate the quality of a diverse range of models in m...

**  Nuit Blanche is now on Twitter: @NuitBlog  ** Shailesh  (also Shailesh1729 ) sent me the following a few months ago: Hi Igor, I have been a regular reader of your blog Nuit Blanche. I would like to draw your attention to a fast C OMP implementation with MATLAB interfaces written by me. Its documentation is available here . It is up to 4 times faster than the OMP implementation in OMPBOX . I hope you will find this information useful and worth sharing on your blog / twitter. I have written fast C implementations of other algorithms whose documentation I am updating currently. With regards, - Shailesh Thanks  Shailesh  ! Of obvious interest is his Sparse-Plex library  to learn about Compressive Sensing. It is here . Follow @NuitBlog  or join the CompressiveSensing Reddit , the Facebook page , the Compressive Sensing group on  LinkedIn    or the Advanced Matrix Factorization group on  Li...

**  Nuit Blanche is now on Twitter: @NuitBlog  ** Differentially Private Compressive k-Means by Vincent Schellekens , Antoine Chatalic , Florimond Houssiau , Yves-Alexandre De Montjoye , Laurent Jacques , Rémi Gribonval This work addresses the problem of learning from large collections of data with privacy guarantees. The sketched learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, from which the learning task is then performed. We modify the standard sketching mechanism to provide differential privacy, using addition of Laplace noise combined with a subsampling mechanism (each moment is computed from a subset of the dataset). The data can be divided between several sensors, each applying the privacy-preserving mechanism locally, yielding a differentially-private sketch of the whole dataset when reunited. We apply this framework to the k-means clustering problem, for which a meas...

**  Nuit Blanche  is now on Twitter:  @NuitBlog  ** Using Machine learning techniques to perform machine learning computation, I really like the meta aspect of this paper.  Estimating the inverse trace using random forests on graphs by  Simon Barthelmé , Nicolas Tremblay , Alexandre Gaudillière , Luca Avena , Pierre-Olivier Amblard Some data analysis problems require the computation of (regularised) inverse traces, i.e. quantities of the form $\Tr (q \bI + \bL)^{-1}$. For large matrices, direct methods are unfeasible and one must resort to approximations, for example using a conjugate gradient solver combined with Girard's trace estimator (also known as Hutchinson's trace estimator). Here we describe an unbiased estimator of the regularized inverse trace, based on Wilson's algorithm, an algorithm that was initially designed to draw uniform spanning trees in graphs. Our method is fast, easy to implement, and scales to very large matrices. Its main draw...

**  Nuit Blanche  is now on Twitter:  @NuitBlog  **  This is the third Workshop «Imaging and Machine Learning» within the Mathematics of Imaging series organized in Paris this semester (videos of Workshop 1 are here , videos of Workshop 2 are here ) 41:31 Structured prediction via implicit embeddings - Alessandro Rudi - Workshop 3 - CEB T1 2019 55:19 A Kernel Perspective for Regularizing Deep Neural Networks - Julien Mairal - Workshop 3 - CEB T1 2019 51:54 Optimization meets machine learning for neuroimaging - Alexandre Gramfort - Workshop 3 - CEB T1 2019 45:58 Random Matrix Advances in Machine Learning - Romain Couillet - Workshop 3 - CEB T1 2019 45:48 Iterative regularization via dual diagonal descent - Silvia Villa - Workshop 3 - CEB T1 2019 38:49 Scalable hyperparameter transfer learning - Valerio Perrone - Workshop 3 - CEB T1 2019 45:58 Using structure to select features in high dimension. - Chloe-Agathe Az...