Reverse Dependencies of autograd

The following projects have a declared dependency on autograd:

• adaptdl — Dynamic-resource trainer and scheduler for deep learning
• adaptdl-modified-pandyaka — Dynamic-resource trainer and scheduler for deep learning
• agjax — A jax wrapper for autograd-differentiable functions.
• aidevlog — Templete of AIDevLog
• alphacore — Core statistical functions for alpha
• apd-crs — apd-crs is a Python package for survival analysis with cure rate, released by Aimpoint Digital, LP
• autograd-gamma — Autograd compatible approximations to the gamma family of functions
• baconian — model-based reinforcement learning toolbox
• Blauwal3 — 蓝鲸，一个基于组件的数据挖掘框架。
• btyd — Buy Till You Die and Customer Lifetime Value statistical models in Python.
• cdopt — A Python package for optimization on closed Riemannian manifolds
• cellgeometry — Statistical Cell Shape Analysis
• ceviche — Ceviche
• ceviche-challenges — no summary
• compas-cem — The Combinatorial Equilibrium Modeling framework for COMPAS
• convoys2 — Implementation of statistical models to analyze time lagged conversions
• crikit — Constitutive Relation Inference Toolkit
• CWGP — CWGP
• debiased-spatial-whittle — Spatial Debiased Whittle likelihood for fast inference of spatio-temporal covariance models from gridded data
• deepbench — Physics Benchmark Dataset Generator
• disropt — DISROPT: a python framework for distributed optimization
• dks — A Python library implementing the DKS/TAT neural network transformation method.
• dynast — DyNAS-T (Dynamic Neural Architecture Search Toolkit) - a SuperNet NAS optimization package
• escnn — E(n)-Equivariant CNNs Library for PyTorch
• escnn_escience — E(n)-Equivariant CNNs Library for PyTorch
• f3dasm — f3dasm - Framework for Data-driven Development and Analysis of Structures and Materials
• fftoptionlib — FFT-based Option Pricing Method in Python
• gameanalysis — A python module for analyzing sparse and empirical games
• gdsfactory — python library to generate GDS layouts
• geomstats — Geometric statistics on manifolds
• gpcsd — Gaussian process current source density estimation
• gplugins — gdsfactory plugins
• grad-descent-visualizer — A package for visualizing test functions and gradient descent.
• grcwa — Rigorous coupled wave analysis supporting automatic differentation with autograd
• halophot — K2 halo photometry with total variation.
• hybrid-vector-model — Package to model the traffic of invasive species and disease vectors
• hyppo — A comprehensive independence testing package
• inferactively-pymdp — A Python package for solving Markov Decision Processes with Active Inference
• isqopen — isq quantum kernel
• isqtools — python tools for isQ
• jaxwell — Jaxwell is JAX + Maxwell: an iterative solver for solving the finite-difference frequency-domain Maxwell equations on NVIDIA GPUs
• lifelines — Survival analysis in Python, including Kaplan Meier, Nelson Aalen and regression
• Lifetimes — Measure customer lifetime value in Python
• LinearResponseVariationalBayes — Helper functions for linear response variational Bayes
• lohrasb — This versatile tool streamlines hyperparameter optimization in machine learning workflows.It supports a wide range of search methods, from GridSearchCV and RandomizedSearchCVto advanced techniques like OptunaSearchCV, Ray Tune, and Scikit-Learn Tune.Designed to enhance model performance and efficiency, it's suitable for tasks of any scale.
• madness-deblender — Galaxy deblender from variational autoencoders
• matchernet — no summary
• metabci — A Library of Datasets, Algorithms, and Experiments workflow for Brain-Computer Interface
• mfrpy — An open sourece DSGE solver under uncertainty
• mici — MCMC samplers based on simulating Hamiltonian dynamics on a manifold.
• mirapy — Python package for Deep Learning in Astronomy
• Mixture-Models — A Python library for fitting mixture models using gradient based inference
• ml-uncertainty — Uncertainty quantification and model inference for machine learning models
• mlaction — Statistical Learning Method package
• mlduct — A personal framework for Machine Learning Pipelines.
• mvdh — Miscellaneous Python tools
• nannos — Fourier Modal Method for multilayer metamaterials
• NANO-filter — Nonlinear Bayesian Filtering with Natural Gradient Gaussian Approximation
• naszilla — python framework for NAS algorithms on benchmark search spaces
• navicat-mikimo — microkinetic modeling code for homogeneous catalytic reactions
• neuroflex — An advanced neural network framework with interpretability, generalization, robustness, and fairness features
• nevergrad — A Python toolbox for performing gradient-free optimization
• noload — solving constrained optimization problem for the design of engineering systems
• numdiff — Numerical backends in Python
• openqaoa-core — OpenQAOA is a python open-source multi-backend Software Development Kit to create, customise and execute the Quantum Approximate Optimisation Algorithm (QAOA) on Noisy Intermediate-Scale Quantum (NISQ) devices, and simulators
• opticalpy — Exact geometrical optics including dispersion to learn and design optical instruments
• paragami — Python pacakge to flatten and fold parameter data structures.
• PennyLane — PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
• phinka — Phinka tools for data processing
• phlearn — A package for simulating and learning pseudo-Hamiltonian systems. For further details, see https://arxiv.org/pdf/2206.02660.pdf and https://arxiv.org/abs/2304.14374
• POT — Python Optimal Transport Library
• prefab — Artificial nanofabrication of integrated photonic circuits using deep learning
• py-wake — PyWake a collection of wake models
• pyabc — Distributed, likelihood-free ABC-SMC inference
• PyBindingCurve — Protein ligand binding simulation in Python
• pyBregMan — A Python library for geometric computing on BREGman MANifolds with applications.
• PyCO2SYS — PyCO2SYS: marine carbonate system calculations in Python
• pydygp — no summary
• pyerrors — Error propagation and statistical analysis for Monte Carlo simulations
• pymanopt — Toolbox for optimization on Riemannian manifolds with support for automatic differentiation
• pymoo — Multi-Objective Optimization in Python
• pymop — Multi-Objective Optimization Problems
• pypesto — python-based Parameter EStimation TOolbox
• pypetal — A pipeline for estimating AGN time lags
• pyquafu — Python toolkit for Quafu-Cloud
• pyrobolearn — A Python framework for roboticists and machine learning practitioners
• pysisyphus — Python suite for exploring potential energy surfaces.
• PythonicDISORT — Discrete Ordinates Solver for the (1D) Radiative Transfer Equation in a single or multi-layer atmosphere.
• qiskit-addon-aqc-tensor — Approximate quantum compilation with tensor networks
• quant1x — Quant1X量化交易框架
• rcwa — Python Implementation of Rigorous Coupled Wave Analysis
• reliability — Reliability Engineering toolkit for Python
• sampyl-mcmc — MCMC Samplers in Python & Numpy
• sax — Autograd and XLA for S-parameters
• schrodinet — Solving the Schrodinger equation using RBF Neural Net
• sciapy — Python tools for (some) SCIAMACHY data
• scikit-fairness — a collection of utilities to make algorithms more fair
• sed-ecfp — Predicting Bioactivities of Ligand Molecules Targeting G Protein-coupled Receptors by Merging Sparse Screening of Extended Connectivity Fingerprints and Deep Neural Nets
• seldonian-engine — Core library for Seldonian algorithms
• slmethod — Statistical Learning Method package