#计算机科学#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
#计算机科学#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 dat...
Simple and efficient Python package for modeling d-dimensional Bravais lattices in solid state physics.
#计算机科学#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
#计算机科学#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
#计算机科学#DataHigh: A graphical user interface for visualizing and interacting with high-dimensional neural activity
Bayesian optimization with Standard Gaussian Processes on high dimensional benchmarks
#计算机科学#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
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.
#计算机科学#Time-HD-Lib: A Library for High-Dimensional Time Series Forecasting
#时序数据库#Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2024) for network estimation and forecasting of high-dimensional time series