high-dimensional

A Flexible and Powerful Parameter Server for large-scale machine learning

Octree/Quadtree/N-dimensional linear tree

Implements "Clustering a Million Faces by Identity"

Open and explore HDF5 files in JupyterLab. Can handle very large (TB) sized files, and datasets of any dimensionality

A fast, accurate, and modularized dimensionality reduction approach based on diffusion harmonics and graph layouts. Escalates to millions of samples on a personal laptop. Adds high-dimensional big data intrinsic structure to your clustering and data visualization workflow.

Simple and efficient Python package for modeling d-dimensional Bravais lattices in solid state physics.

A numerical library for High-Dimensional option Pricing problems, including Fourier transform methods, Monte Carlo methods and the Deep Galerkin method

Particle Swarm Optimization Visualization

DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems

Numerical illustration of a novel analysis framework for consensus-based optimization (CBO) and numerical experiments demonstrating the practicability of the method

[TMLR' 24] High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy

DataHigh: A graphical user interface for visualizing and interacting with high-dimensional neural activity

BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data

Bayesian optimization with Standard Gaussian Processes on high dimensional benchmarks

Controlled Invariant Sets in Two Moves

Regularization Paths for Huber Loss Regression and Quantile Regression Penalized by Lasso or Elastic-Net

KNRScore is a Python package for computing K-Nearest-Rank Similarity, a metric that quantifies local structural similarity between two maps or embeddings.

SpokeDarts sphere-packing sampling in any dimension. Advancing front sampling from radial lines (spokes) through prior samples.

Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2024) for network estimation and forecasting of high-dimensional time series

Video Input Generative Adversarial Imitation Learning