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GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process models. It provides a system for Gaussian process modeling and uncertainty estimation, designed to perform efficient matrix operations on graphics hardware. The framework features a modular kernel system for constructing custom covariance functions and modeling complex data dependencies. It specifically integrates Gaussian processes with deep neural networks to create hybrid models for regression and classification. The system employs numerical linear algebra techniques, inclu
TorchANI 2.0 is an open-source library that supports training, development, and research of ANI-style neural network interatomic potentials. It was originally developed and is currently maintained by the Roitberg group.
Differentiable rendering without approximation.
The main features of stanfordvl/minkowskiengine are: Deep Learning Architectures, Deep Learning Frameworks, Data and Graph Processing, Developer Tools.
Open-source alternatives to stanfordvl/minkowskiengine include: cornellius-gp/gpytorch — GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process… charlesq34/pointnet — PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without… aiqm/torchani — TorchANI 2.0 is an open-source library that supports training, development, and research of ANI-style neural network… bachili/redner — Differentiable rendering without approximation. catalyst-team/catalyst — Accelerated deep learning R&D. bharathgs/nalu.