chandar-lab/PatchUp

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Sushmitha-93/CMPE275---Machine-Learning-Assigments-and-Labs

Machine Learning Course Topic wise Assignments and Projects

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adeyemiadeoye/SelfConcordantSmoothOptimization.jl

Self-concordant Smoothing for Large-Scale Convex Composite Optimization

Language: Julia - Size: 203 MB - Last synced: about 1 month ago - Pushed: about 1 month ago - Stars: 0 - Forks: 0

FaresGh1997/MLDM_HWs

Machine Learning and Data Mining Projects (2022-2023)

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alecruces/SafeRoadsCA

Analyzing car accident fatalities to pave the way for preventative measures and safer transportation using Statistical and Machine Learning algorithms

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koushik16/Bias-Variance-Decomposition

Implementing bias-variance-noise decomposition on binary data from Gaussian distributions. Create functions for noise, bias, and variance computation, leveraging Bayes' rule, ridge regression, and model averaging. Aim to visualize error changes with regularization.

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magnusneuman/Modular-GLASSO

Code for the paper "Module-based regularization improves Gaussian graphical models when observing noisy data"

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ez77/Numerical-Methods

Repository with some implementations of algorithms used in Numerical Analysis. From the solution of determined and overdeterminded systems to regularization and non-linear least square problems.

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Tikhon-Radkevich/CourseYandexML

These training sessions in machine learning, conducted by Yandex, are dedicated to classical machine learning. This offers an opportunity to reinforce theoretical knowledge through practice on training tasks.

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KristofPusztai/MixReg

Stable-Baselines Implementation of MixReg regularization technique for PPO2

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mdpetters/RegularizationTools.jl

A Julia package to perform Tikhonov regularization for small to moderate size problems.

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LamineTourelab/Drug-discovery

I am working on the discovery of new potential therapeutic tagets using Machine learning on multi-omics data.

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devanshkhare1705/Personalizing-K12-Education

Using deep learning to predict whether students can correctly answer diagnostic questions

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kk289/ML-Regularized_Linear_Regression-Bias_Variance-MATLAB

Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.

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abs711/Optimizing-representations-for-gait-generation

Using encoder-decoder neural networks to learn representations of personal walking style, and generating person-specific gait for desired activities.

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Luehrsen/sd_regularization_images

Pre-Rendered Regularization Images fou use with fine-tuning, especially for the current implementation of "Dreambooth"

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AlePalu/fdaPDE Fork of fdaPDE/fdaPDE

fdaPDE: Physics-Informed Spatial and Functional Data Analysis

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ar8372/CutMix-Regularization-Strategy-to-Train-Strong-Classifiers-with-Localizable-Features

Through this project we will try to understand CutMix by implementing it on a simple problem of cat-vs-dog classification.

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seunshix/convolutional_neural_networks

Preprocessed the USPS dataset, implemented and compared different network architectures and optimization techniques, applied regularization techniques such as ensembling and dropout, performed adversarial training to evaluate network robustness, and evaluated network performance using metrics such as accuracy, precision, and recall.

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Distraict/Stable-Diffusion-Regularization-Images

A vast assortment of class regularization images in sets of 1500

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mohadeseh-ghafoori/Regularization-Methods

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CarolinaPerdomo/Thesis

Notebooks developed in Mathematica for my Ph.D. thesis and other resources

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mbidu/Machine-Learning-Projects

Machine Learning Projects

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faketano/Determining-critical-components-for-development

Use of Supervised and Unsupervised Learning Techniques to determine critical components for development of countries

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aliyzd95/Optimization-and-Regularization-from-scratch

Implementation of optimization and regularization algorithms in deep neural networks from scratch

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dizam92/pyTorchReg

Applied Sparse regularization (L1), Weight decay regularization (L2), ElasticNet, GroupLasso and GroupSparseLasso to Neuronal Network.

Language: Python - Size: 3.15 MB - Last synced: about 1 year ago - Pushed: about 2 years ago - Stars: 36 - Forks: 8

sauriii98/Deep-Learning-algorithms

Implementation of all basic algorithms needed in Deep Learning

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inimah/protoinfomax

Code and Data sets for the EMNLP-2021-Findings Paper "ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection"

Language: Python - Size: 24.4 MB - Last synced: about 1 year ago - Pushed: about 2 years ago - Stars: 3 - Forks: 3

victor7246/WNUT-2020-Task-2

System developed by team datamafia in WNUT 2020 Task 2: Identification of informative COVID-19 English Tweets

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GeorgeVern/AFTERV1.0

Python source code for EMNLP 2020 Findings paper: "Domain Adversarial Fine-Tuning as an Effective Regularizer".

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steve303/stat542code2-RegularizationMethods

Objectives: 1) Implement coordinate descent algorithm for lasso penalization using R, 2) Document performance differences between lasso and ridge penalization when performing regression on a reference data set versus reference data with added spurious predictors

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dipterix/spfda

Functional Group Bridge for Simultaneous Regression and Support Estimation (https://arxiv.org/abs/2006.10163)

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ashishpatel26/Regularization-Collection-Deeplearning

This is Collection of Regularization Deep learning techniques with code and paper

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EliottZemour/cs-433-project-2-lpmc_dcm Fork of CS-433/cs-433-project-2-lpmc_dcm

Regularized maximum likelihood estimation for discrete choice models on the LPMC dataset

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rpatrik96/lod-wmm-2019

Supplementary code for the paper "Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks" - an accepted paper of LOD2019

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DataEngel/World-Happiness-Report--Robust-regressions

Constructing a linear regression model which may help us predict the happiness score in the 155 countries.

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cassnutt/Detecting_pneumonia

Using over 5,800 images of chest radiographs, I utilized machine learning and neural networks to predict when pneumonia is present. The best model was able to predict over 80% accuracy on the test data with a false negative rate that was less than 3%.

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Subangkar/Improving-Deep-Neural-Networks-Deeplearning.ai-Coursera-Assignments

Notebooks of programming assignments of Improving Deep Neural Networks course of deeplearning.ai on coursera in August-2019

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KrystynaGrzesiak/gslope

Sparse Gaussian graphical models with Sorted L-One Penalized Estimation

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alejandrods/Noise-Regularization-Method-Neural-Network

Adding noise as regularization method to reduce overffiting in neural networks

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