Popular GitHub Repositories for Pearson Correlation

A Python library that helps data scientists to infer causation rather than observing correlation.

About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008

Anomaly Detection and Correlation library

Implementation of "YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception".

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets

Real-time GCC-NMF Blind Speech Separation and Enhancement

CorEx or "Correlation Explanation" discovers a hierarchy of informative latent factors. This reference implementation has been superseded by other versions below.

[AAAI 2022] An official source code for paper Deep Graph Clustering via Dual Correlation Reduction.

收集AIOPS（智能运维），时间序列，异常检测，关联分析，告警收敛，根因分析，数据挖掘，机器学习，深度学习的学习资源。欢迎star。Collect learning resources for AIOPS (Intelligent Operation and Maintenance), time series, anomaly detection, correlation analysis, alarm convergence, root cause analysis, data mining, machine learning, and deep learning. Welcome star.

Framework for correlating two or more well logs using feature vectors generated from CNN's in Pytorch

Disentagnled Graph Collaborative Filtering, SIGIR2020

Official pytorch repository for CG-DETR "Correlation-guided Query-Dependency Calibration in Video Representation Learning for Temporal Grounding"

Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation functionals using machine learning techniques.

[CVPR 2022] Exploring Dual-task Correlation for Pose Guided Person Image Generation

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

Subpixel displacement and strain fields estimation with deep learning

A project using pyspider to collect data and NLP techs to analyze the correlation among the data

An Enhanced Implementation of the Colour Transfer Method Proposed by E Reinhard et al.