Julien Mairal - Software
SPAMS is an optimization toolbox implementing algorithms to address various machine learning and signal processing problems involving dictionary learning and matrix factorization (e.g., NMF, sparse PCA); solving medium-scale sparse decomposition problems with LARS, coordinate descent, OMP, SOMP, proximal methods; solving large-scale sparse estimation problems with stochastic optimization; solving structured sparse decomposition problems (e.g., sparse group lasso, tree-structured regularization, structured sparsity with overlapping groups).
FlipFlop is an open-source software, implementing a fast method for de novo transcript discovery and abundance estimation from RNA-Seq data. It differs from classical approaches such as Cufflinks by simultaneously performing the identification and quantitation tasks using a penalized maximum likelihood approach, which leads to improved precision recall. Other software taking this approach have an exponential complexity in the number of exons of a gene. We use a novel algorithm based on network flow formalism, which gives us a polynomial runtime. In practice, FlipFlop was shown to outperform penalized maximum likelihood based software in terms of speed, and to perform transcript discovery in less than 1/2 second for large genes. FlipFlop 1.0.0 is a user friendly bioconductor R package. It is freely available on the Bioconductor website.
This is the open-source software package corresponding to the ICML’16 paper “Dictionary Learning for Massive Matrix Factorization” for huge-scale matrix factorization. The package is written and maintained by Arthur Mensch (Inria). This is a highly optimized library that is able to handle matrices of several terabytes on a single workstation.
This is the open-source software package corresponding to the paper “Convolutional kernel networks” published at NIPS’14. We are going to release soon a new GPU implementation corresponding to the NIPS’16 paper “End-to-end kernel learning with supervised convolutional kernel networks”.
This is the software package and the new dataset “RomePatches” corresponding to the IJCV paper “Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach”.
This is the open-source software package corresponding to the paper IEEE-TSP paper “DOLPHIn-Dictionary Learning for Phase Retrieval”. The package is written and maintained by Andreas Tillmann (TU Darmstadt).