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Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through R&D-Agent, which lets AI drive data-driven AI. 🔗https://aka.ms/RD-Agent-Tech-Report
A Python implementation of global optimization with gaussian processes.
Accelerated deep learning R&D
Optax is a gradient processing and optimization library for JAX.
MLBox is a powerful Automated Machine Learning python library.
ObsPy: A Python Toolbox for seismology/seismological observatories.
Module for statistical learning, with a particular emphasis on time-dependent modelling
Molecular Processing Made Easy.
Tools for computational pathology
PyTorch Connectomics: segmentation toolbox for EM connectomics
Safe Bayesian Optimization
Nellie: Automated organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy
The Harmony Python library: a research tool for psychologists to harmonise data and questionnaire items. Open source.
A fast MDCT implementation using SciPy and FFTs
Python Frontend to Algorithms of the Algorithm Engineering Group Heidelberg
Accurate segmentation of bacterial microscope images using deep learning synthetically generated image data.