Instant neural graphics primitives: lightning fast NeRF and more
-
Updated
Apr 18, 2024 - Cuda
Instant neural graphics primitives: lightning fast NeRF and more
Library for multivariate function approximation with splines (B-spline, P-spline, and more) with interfaces to C++, C, Python and MATLAB
Fast radial basis function interpolation for large scale data
A collection of B-spline tools in Julia
CSE 571 Artificial Intelligence
Reinforcement learning algorithms
TorchQuantum is a backtesting framework that integrates the structure of PyTorch and WorldQuant's Operator for efficient quantitative financial analysis.
Adaptively sampled distance fields in Julia
Julia Wrapper to the Tasmanian library
Basis Function Expansions for Julia
Julia library for function approximation with compact basis functions
An adaptive fast function approximator based on tree search
The tools for proper interactions between ApproxFun.jl and DifferentialEquations.jl for pseudospectiral partial differential equation discretizations in scientific machine learning (SciML)
Easy21 assignment from David Silver's RL Course at UCL
Multivariate Normal Hermite-Birkhoff Interpolating Splines in Julia
Code repository with classical reinforcement learning and deep reinforcement learning methods for Pokémon battles in Pokémon Showdown.
Suite of 1D, 2D, 3D demo apps of varying complexity with built-in support for sample mesh and exact Jacobians
Python framework to approximate mathemtical functions
Simple linear regressor that tries to approximate a simple function deployed in Tensorflow 2.0 without Keras
Add a description, image, and links to the function-approximation topic page so that developers can more easily learn about it.
To associate your repository with the function-approximation topic, visit your repo's landing page and select "manage topics."