Popular GitHub Repositories for Importance Resampling

? 目前最大的工业缺陷检测数据库及论文集 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance.

XAI - An eXplainability toolbox for machine learning

Boltzmann Machines in TensorFlow with examples

Code for SuDoRm-Rf networks for efficient audio source separation. SuDoRm-Rf stands for SUccessive DOwnsampling and Resampling of Multi-Resolution Features which enables a more efficient way of separating sources from mixtures.

The Noise Contrastive Estimation for softmax output written in Pytorch

DSIR large-scale data selection framework for language model training

AI Super - Resolution

Toolkit for Apache Spark ML for Feature clean-up, feature Importance calculation suite, Information Gain selection, Distributed SMOTE, Model selection and training, Hyper parameter optimization and selection, Model interprability.

An AviSynth+ implementation of some Super-Resolution Convolutional Neural Networks.

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" ? (ICLR 2019)

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Code for the EMNLP 2021 Paper "Active Learning by Acquiring Contrastive Examples" & the ACL 2022 Paper "On the Importance of Effectively Adapting Pretrained Language Models for Active Learning"

Beta Machine Learning Toolkit

SurvSHAP(t): Time-dependent explanations of machine learning survival models

An interactive framework to visualize and analyze your AutoML process in real-time.

Analyzes your codebase identifying important files based on dependency relationships. Generates diagrams and importance scores per file, helping AI assistants understand the codebase. Automatically parses popular programming languages such as Python, C, C++, Rust, Zig, Lua.

Neural Network-Boosted Importance Nested Sampling for Bayesian Statistics

Code for NeurIPS'19 "Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks"

Clustering with variational Bayes and population Monte Carlo

Create modular, cross-browser, web audio pipelines to record and process audio in background threads. Comes with modules for VAD, ASR, resampling and much more...