This is a re-implementation of the convolutional kernel network (CKN) methods introduced in Julien Mairal. End-to-End Kernel Learning with Supervised Convolutional Kernel Networks. Adv. NIPS. 2016. This is an almost pure C implementation using directly CUDA and CUDNN, along with a Matlab interface. The software package features both the unsupervised and supervised variants of CKNs and is open-source with a GPLv3 license.


CKN-seq is a software package to model biological sequences with convolutional kernel networks. The current implementation corresponds to the BiorXiv preprint "Predicting Transcription Factor Binding Sites with Convolutional Kernel Networks". It was written by Dexiong Chen (Inria).


This is a software package for local ancestry inference corresponding to the BiorXiv preprint “Loter: A Software Package to Infer Local Ancestry for a Wide Range of Species”. The package is written and maintained by Thomas Dias-Alves (Univ. Grenoble Alpes).


C++ library with Cython bindings for fast stochastic optimization algorithms for machine learning, including stochastic approximation, variance reduction and hybrid algorithms like stochastic MISO (Bietti and Mairal, 2017) for handling finite datasets with random perturbations.


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 IEEE-TSP paper "DOLPHIn-Dictionary Learning for Phase Retrieval". The package is written and maintained by Andreas Tillmann (TU Darmstadt).


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 "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".


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 under a GPL license.

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