Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
- autodiff
- deep-learning
- machine-learning
- rust
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Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
Bayesian optimization in PyTorch
Adaptive Experimentation Platform
Vulkan/CUDA/HIP/OpenCL/Level Zero/Metal Fast Fourier Transform library
A research toolkit for particle swarm optimization in Python
Public/backup repository of the GROMACS molecular simulation toolkit. Please do not mine the metadata blindly; we use https://gitlab.com/gromacs/gromacs for code review and issue tracking.
:four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
🎯 A comprehensive gradient-free optimization framework written in Python
A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
Dicoogle - Open Source PACS
PyKEP is a scientific library providing basic tools for research in interplanetary trajectory design.
Produce a sharp image of a planetary system object (moon, sun, planets) from many seeing-affected frames according to the "lucky imaging" technique.
Native R and Python sample-deletion robustness for omics: candidate certificates, minimum deletion bounds, Bioconductor model refits and donor-level pseudobulk
A command-line utility for quick math.
Interpretable bilinear attention network with domain adaptation improves drug-target prediction.
ENIGMA HALFpipe is a user-friendly software that facilitates reproducible analysis of fMRI data
A strongly-typed genetic programming framework for Python
Graph data structures with multiple heterogeneous metadata for Graphs.jl.
An extensible .NET genetic algorithm library for optimization and artificial intelligence - Evolutionary computation and complex problem solving made simple
A metaheuristic optimization framework
Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning
Vendor-agnostic, FHIR-Genomics-aligned API contract for cross-vendor NGS interpretation. JSON Schema + TypeScript + Python pydantic.
This is a class module in beta state to implement computations with large integers and reals/rationals numbers, while proposing representation of numerical values as scientific notation, allowing to obtain several cohorts for the same number (a lot of fun!).
Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.