Python

Loter2: Genome-wide local ancestry inference in admixed individuals with scalable penalized nearest neighbor algorithm

In most Eukaryotes species, the transmission of genetic materials between generations is achieved through sexual reproduction. During this process, each individual inherits half of their genome from both their parents. Thanks to genetic recombination, the genome of an individual is a non-uniform combination of the genetic material of their ancestors. This process directly impacts individual phenotype transmission and species or population evolution. During inter-population (or inter-species) breeding events, descendants inherit an admixture of genetic materials from both source populations (or species).

Genome-wide local ancestry inference in admixed individuals with scalable penalized nearest neighbor algorithm

In most Eukaryotes species, the transmission of genetic materials between generations is achieved through sexual reproduction. During this process, each individual inherits half of their genome from both their parents. Thanks to genetic recombination, the genome of an individual is a non-uniform combination of the genetic material of their ancestors. This process directly impacts individual phenotype transmission and species or population evolution. During inter-population (or inter-species) breeding events, descendants inherit an admixture of genetic materials from both source populations (or species).

Seamless Kernel Operations on GPU, with auto-differentiation and without memory overflows

The KeOps library (http://www.kernel-operations.io) provides routines to compute generic reductions of large 2d arrays whose entries are given by a mathematical formula. Using a C++/CUDA-based implementation with GPU support, it combines a tiled reduction scheme with an automatic differentiation engine. Relying on online map-reduce schemes, it is perfectly suited to the scalable computation of kernel dot products and the associated gradients, even when the full kernel matrix does not fit into the GPU memory.

KeOps: seamless Kernel Operations on GPU, with auto-differentiation and without memory overflows

The KeOps library (http://www.kernel-operations.io) provides routines to compute generic reductions of large 2d arrays whose entries are given by a mathematical formula. Using a C++/CUDA-based implementation with GPU support, it combines a tiled reduction scheme with an automatic differentiation engine. Relying on online map-reduce schemes, it is perfectly suited to the scalable computation of kernel dot products and the associated gradients, even when the full kernel matrix does not fit into the GPU memory.

KeOps

Seamless Kernel Operations on GPU with auto-differentiation and without memory overflows

Loter

Python package for haplotype phasing and local ancestry inference

SPAMS

Optimization toolbox for sparse estimation, implementing algorithms that solve machine learning and signal processing problems involving sparse regularizations.

ckntf

Re-implementation of Convolutional Kernel Network (CKN) from Mairal (2016) in Python based on the TensorFlow framework