Popular GitHub Repositories for Bitwise Operators

Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.

Learning in infinite dimension with neural operators.

High-efficiency floating-point neural network inference operators for mobile, server, and Web

Several simple examples for popular neural network toolkits calling custom CUDA operators.

Infinity ∞ : Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

PDEBench: An Extensive Benchmark for Scientific Machine Learning

Physics-Informed Neural networks for Advanced modeling

OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM

Low-Level Graph Neural Network Operators for PyG

? Yet another operator for running large language models on Kubernetes with ease. Powered by Ollama! ?

This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs

A Library for Advanced Neural PDE Solvers.

A Simple & Flexible Cross Framework Operators Toolkit

The first autonomous computer program that can do anything to earn money without human operators.

MSVBASE is a system that efficiently supports complex queries of both approximate similarity search and relational operators. It integrates high-dimensional vector indices into PostgreSQL, a relational database to facilitate complex approximate similarity queries.

This repo covers Kubeflow Environment with LABs: Kubeflow GUI, Jupyter Notebooks on pods, Kubeflow Pipelines, Experiments, KALE, KATIB (AutoML: Hyperparameter Tuning), KFServe (Model Serving), Training Operators (Distributed Training), Projects, etc.

[ICCV 2023] When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo

Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."

The Natural Language Shell integrates OpenAI's GPTs, Anthropic's Claude, or local GGUF-formatted LLMs directly into the terminal experience, allowing operators to describe their tasks in either POSIX commands or fluent human language

Tensor decomposition with arbitrary expressions: inner, outer, elementwise operators; nonlinear transformations; and more.