A Flexible and Powerful Parameter Server for large-scale machine learning
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Updated
Jan 16, 2024 - Java
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.
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
Numerical illustration of a novel analysis framework for consensus-based optimization (CBO) and numerical experiments demonstrating the practicability of the method
DataHigh: A graphical user interface for visualizing and interacting with high-dimensional neural activity
[TMLR' 24] High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy
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
SpokeDarts sphere-packing sampling in any dimension. Advancing front sampling from radial lines (spokes) through prior samples.
flameplot is a python package for the quantification of local similarity across two maps or embeddings.
Bayesian optimization with Standard Gaussian Processes on high dimensional benchmarks
DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems
Video Input Generative Adversarial Imitation Learning
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