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

A high-performance distributed deep learning system targeting large-scale and automated distributed training.

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.

Octree/Quadtree/N-dimensional linear tree

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

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

Particle Swarm Optimization Visualization

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

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

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

Controlled Invariant Sets in Two Moves

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

flameplot is a python package for the quantification of local similarity across two maps or embeddings.

Solution Paths of Sparse Linear Support Vector Machine with Lasso or ELastic-Net Regularization

Video Input Generative Adversarial Imitation Learning

Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2022) for network estimation and forecasting of high-dimensional time series

Official Implementation of On Optimal Private Online Stochastic Optimization and High Dimensional Decision Making

Biomarker selection in penalized regression models