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9 search results found

Pyramid ⭐ 162

Open source Machine Learning library written in Java

Continual Learning Benchmark ⭐ 158

Evaluate three types of task shifting with popular continual learning algorithms.

Pure Python implementation of the Follow The Regularized Leader - Proximal algorithm

Efficient Algorithms for L0 Regularized Learning

Optical Flow ⭐ 42

Pure python implementation of Gunnar Farneback's optical flow algorithm

Code implementing the CORE-RL algorithm with DDPG, PPO, and TRPO. See the paper "Control Regularization for Reduced Variance Reinforcement Learning" for additional details.

Depthinpainting ⭐ 21

Depth Image Inpainting with Low Gradient Regularization

Ksvd_reg ⭐ 20

Regularized K-SVD Algorithm

Cure_robustness ⭐ 18

Implemented CURE algorithm from robustness via curvature regularization and vice versa

Simple Implementation Of Ml Algorithms ⭐ 17

My simplest implementations of common ML algorithms

Cubic_reg ⭐ 12

Implementation of Nesterov and Polyak's (2006) cubic regularization algorithm and Cartis et al's (2011) adaptive cubic regularization algorithm

Bpe Dropout ⭐ 12

An official implementation of "BPE-Dropout: Simple and Effective Subword Regularization" algorithm.

Machine Learning From Scratch ⭐ 11

Popular machine learning algorithms, including GBDT, SVM and NN, implemented with simple python code.

Optimal Transport ⭐ 7

Group project "Algorithms for large-scale optimal transport". Implement ADMMs and Sinkhorn's Algorithms.

[PY]thon [R]ec[O]mmender [S]ystems library

Machine Learning Resources ⭐ 7

This cover everything you need to know if you want to learn Machine Learning from basics to advance. It covers how to do exploratory data analysis over datasets, build machine learning models, evaluate their performance and deploy them.

Constrainedlasso.jl ⭐ 6

Algorithms for fit constrained lasso

SON-EM - Algorithm for parameter estimation of hybrid time-varying parameter systems using Sum of Norms regularization and Expectation Maximization

Ccdralgorithm ⭐ 5

Structure learning for Bayesian networks using the CCDr algorithm.

Parallel Implementations of the Empirical Bayesian Elastic Net Cross-Validation in R

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