57 Open source projects that are alternatives of or similar to uapca

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

Federated Principal Component Analysis Revisited!

A collection of Wells/Drilling Engineering tools, focused on well trajectory planning for the time being.

A repository of resources for understanding the concepts of machine learning/deep learning.

Comparison of dimensionality reduction methods

A Python Library for Similarity-based Dimensionality Reduction

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020

A fast and flexible framework for data reduction in R

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

BioSTEAM's Premier Repository for Biorefinery Models and Results

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

A random forest

Uncertainty-Wizard is a plugin on top of tensorflow.keras, allowing to easily and efficiently create uncertainty-aware deep neural networks. Also useful if you want to train multiple small models in parallel.

Easy genetic ancestry predictions in Python

(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.

Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)

Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.

🕝 Time-warped principal components analysis (twPCA)

Using Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.

[ICCV'19] Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty

noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.

[CVPR'21] MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation

Model data as beliefs (at a certain time) about events (at a certain time).

A library for uncertainty quantification based on PyTorch

Deep learning AI, that recognizes when are people uncertain

Sampling nuclear data and uncertainty

Reusable, Easy-to-use Uncertainty module package built with Tensorflow, Keras

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.

A curated list of community detection research papers with implementations.

Uniform Manifold Approximation and Projection

Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.

Online feature-extraction and classification algorithm that learns representations of input patterns.

tGPLVM: A Nonparametric, Generative Model for Manifold Learning with scRNA-seq experimental data

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).

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 Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN

R package for Distance Metric Learning

Uniform Manifold Approximation and Projection (UMAP) implementation in Julia

Parallel Barnes-Hut t-SNE implementation written in Rust.

A computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.

In this repository, You can find the files which implement dimensionality reduction on the hyperspectral image(Indian Pines) with classification.

Estimate dynamic high-order correlations in multivariate timeseries data

ADENINE: A Data ExploratioN PipelINE

Minimum-distortion embedding with PyTorch

A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).

Single-cell Hierarchical Poisson Factorization

R wrappers to connect Python dimensional reduction tools and single cell data objects (Seurat, SingleCellExperiment, etc...)

TLDR is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses

This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

Local Fisher Discriminant Analysis in R

A comprehensive dimensional reduction framework to recover the latent topology from high-dimensional data.

Ensemble topic modelling with pLSA

Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).

Streaming, Memory-Limited, r-truncated SVD Revisited!