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Differentiable rendering without approximation.
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.
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
Quantized Neural Network PACKage - mobile-optimized implementation of quantized neural network operators
The main features of pytorch/qnnpack are: Data and Graph Processing, Developer Tools.
Projects with overlapping indexed features include: bharathgs/nalu. cvxgrp/cvxpylayers — CVXPYlayers is a Python library for constructing differentiable convex optimization layers in PyTorch, JAX, and MLX… 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. cornellius-gp/gpytorch — GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process… dmlc/dgl — DGL is a Python library for building and training graph neural networks. It functions as a graph message passing…