Popular GitHub Repositories for Spearman Rank Correlation

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

[Tensorflow] High Correlation Data Removing Method for Deep Reinforcement Learning in Obstacle Avoidance and Path Planning

A Python implementation of LightFM, a hybrid recommendation algorithm.

Deep recommender models using PyTorch.

Learning to Rank in TensorFlow

[ICLR2025 Spotlight] SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models

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

A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

Anomaly Detection and Correlation library

allRank is a framework for training learning-to-rank neural models based on PyTorch.

FaceRank - Rank Face by CNN Model based on TensorFlow (add keras version). FaceRank-人脸打分基于 TensorFlow (新增 Keras 版本) 的 CNN 模型（QQ群：167122861）。技术支持：http://tensorflow123.com

The most easy-to-understand tutorial for using LoRA (Low-Rank Adaptation) within diffusers framework for AI Generation Researchers?

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

Automatic summarizer text in Swift

Official code for ReLoRA from the paper Stack More Layers Differently: High-Rank Training Through Low-Rank Updates

Official Pytorch implementation of ReXNet (Rank eXpansion Network) with pretrained models

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