Popular GitHub Repositories for Parametric Curves

Unsupervised Feature Learning via Non-parametric Instance Discrimination

Simulation of path planning for self-driving vehicles in Unity. This is also an implementation of the Hybrid A* pathfinding algorithm which is useful if you are interested in pathfinding for vehicles.

A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks

:sparkles: A plotter for reinforcement learning (RL)

Official code for Dynamic Parametric RAG.

?? Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatments from observational data using neural networks.

Official code repository of "VideoCAD: A Dataset and Model for Learning Long‑Horizon 3D CAD UI Interactions from Video" @ NeurIPS 2025

Clique recycling non-Gaussian (multi-modal) factor graph solver; also see Caesar.jl.

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

CPAB Transformations: finite-dimensional spaces of simple, fast, and highly-expressive diffeomorphisms derived from parametric, continuously-defined, velocity fields in Numpy, Tensorflow and Pytorch

pytorch implementation of Parametric Noise Injection for adversarial defense

Parametric differentiable curves with PyTorch for continuous embeddings, shape-restricted models, or KANs

Statistical inference on machine learning or general non-parametric models

Official repo for separable operator networks -- extreme-scale operator learning for parametric PDEs.

Dimension reduced surrogate construction for parametric PDE maps

GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.

Must-read papers and blogs about parametric knowledge mechanism in LLMs.

Code for our paper "VaPar Synth - A Variational Parametric Model for Audio Synthesis"

Point Projection On Parametric Curves / Trajectory Maps for Autonomous Driving

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"