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
Implements "Clustering a Million Faces by Identity"
A high-performance distributed deep learning system targeting large-scale and automated distributed training.
Open and explore HDF5 files in JupyterLab. Can handle very large (TB) sized files, and datasets of any dimensionality
Particle Swarm Optimization Visualization
Octree/Quadtree/N-dimensional linear tree
Simple and efficient Python package for modeling d-dimensional Bravais lattices in solid state physics.
DataHigh: A graphical user interface for visualizing and interacting with high-dimensional neural activity
Replicate the results of nowcasting housing sales by Google Queries, using Bayesian Structural Time-Series Model (Choi & Varian, 2009, 2012).
A numerical library for High-Dimensional option Pricing problems, including Fourier transform methods, Monte Carlo methods and the Deep Galerkin method
Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2024) for network estimation and forecasting of high-dimensional time series
Controlled Invariant Sets in Two Moves
Numerical illustration of a novel analysis framework for consensus-based optimization (CBO) and numerical experiments demonstrating the practicability of the method
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.
Solution Paths of Sparse Linear Support Vector Machine with Lasso or ELastic-Net Regularization
Codes for Chandra, et al. (2021+). Escaping the curse of dimensionality in Bayesian model based clustering. Please refer to the original paper for details https://arxiv.org/abs/2006.02700
R codes and dataset for the estimation of the high-dimensional state space model proposed in the paper "A dynamic factor model approach to incorporate Big Data in state space models for official statistics" with Franz Palm, Stephan Smeekes and Jan van den Brakel.
A Bayesian multiscale deep learning framework for flows in random media
Regularization Paths for Huber Loss Regression and Quantile Regression Penalized by Lasso or Elastic-Net
Biomarker selection in penalized regression models
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