R package: Hidden Markov Model for Bayesian Inference of large-scale mean and variance testing
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Updated
Dec 9, 2017 - R
R package: Hidden Markov Model for Bayesian Inference of large-scale mean and variance testing
Make HTS Forecasting paper implemented on a M5 dataset
Java based Convolutional Neural Network package running on Apache Spark framework
Universal ML: Large Scale Distributed Machine Learning (Deep Learning) Systems
My thesis
Tutorials on optimizers for deep neural networks
Enitor provides the MATLAB implementation of several large-scale kernel methods.
Repository for non-Mercer multiclass EFS-LS-MCM presented in IJCNN 2018
A curated list of papers on large-scale graph learning.
Falkon is one of the most efficient algorithm able to work in a supervised large scale setting. This method is the result of a combination of three simple principles: sub-sampling, preconditioning and iterative solvers. In order to extend FALKON usability we have designed an extension able to work in a semi-supervised scenario.
Machine Learning Platform Based on PS-Lite
Fast Factorization Machines
Machine learning course by Andrew Ng
Network of Experts for Large-Scale Image Categorization [ECCV 2016]
Code for reproducing the experiments on large-scale pre-training and transfer learning for the paper "Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images" (https://arxiv.org/abs/2106.00116)
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches
LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms
A distributed implementation of "Nested Subtree Hash Kernels for Large-Scale Graph Classification Over Streams" (ICDM 2012).
Scaling Object Detection by Transferring Classification Weights
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