Instant neural graphics primitives: lightning fast NeRF and more
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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
Adaptively sampled distance fields in Julia
Julia Wrapper to the Tasmanian library
TorchQuantum is a backtesting framework that integrates the structure of PyTorch and WorldQuant's Operator for efficient quantitative financial analysis.
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)
Multivariate Normal Hermite-Birkhoff Interpolating Splines in Julia
Easy21 assignment from David Silver's RL Course at UCL
Python framework to approximate mathemtical functions
Suite of 1D, 2D, 3D demo apps of varying complexity with built-in support for sample mesh and exact Jacobians
Simple linear regressor that tries to approximate a simple function deployed in Tensorflow 2.0 without Keras
The focus of function approximation problems has been on identifying some suitable function without attempting to gain insight into the mechanism of the system. The performance of the model boils down to interpolation. But, in a more realistic setting, we expect test data from outside the distribution of the training set. To better extrapolate t…
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