Tonight: Paris Machine Learning #4 Season 4: ML for Ultrasound, export/import in R, electric consumption from times series, #NIPS2016

 

The streaming video for the meetup is here:   All the presentations are listed below:      

Merci au Dojo de nous accueillir et merci à Snips.ai de nous offrir le buffet de ce soir.  
Voici le programme de cette réunion : 

* Session de recrutement de Data-Scientist, Franck Bardol
Collecte de candidatures pour une compagnie  d'assurance via le groupe LinkedIn du Paris Machine Learning

* Projet humanitaire : Machine Learning appliqué aux images d'échographie, Anne-Laure Rousseau
 
* Benchmark: Import/export functions in R, Mathieu Lechine
 It aims at presenting the best ways to import/export data in R. The benchmark compares functions for CSV files (read.csv/write.csv, fread/fwrite from….) and binary files (save/load, read_feather/write_feather…). The comparison takes into account the execution time and the size of the file.

* Reconstituer la consommation d'électricité à partir des agrégations temporelles / Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates, Jiali Mei

Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries. The objective is to estimate multiple time series at a fine temporal scale from temporal aggregates measured on each individual series. Furthermore, our algorithm is extended to take into account individual autocorrelation to provide better estimation, using a recent convex relaxation of quadratically constrained quadratic program. Extensive experiments on synthetic and real-world electricity consumption datasets illustrate the effectiveness of our matrix recovery algorithms.

* So What happened at NIPS 2016 ? Igor Carron.
 

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