There are 0 repository under pruning-algorithms topic.
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
A curated list for Efficient Large Language Models
[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support Llama-3/3.1, Llama-2, LLaMA, BLOOM, Vicuna, Baichuan, TinyLlama, etc.
Automated Identification of Redundant Layer Blocks for Pruning in Large Language Models
A research library for pytorch-based neural network pruning, compression, and more.
[AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models
Model optimizer used in Adlik.
Caffe/Neon prototxt training file for our Neurocomputing2017 work: Fuzzy Quantitative Deep Compression Network
This project provides tools to load and prune large language models using a structured pruning method.
Official code for "EC-SNN: Splitting Deep Spiking Neural Networks on Edge Devices" (IJCAI2024)
KEN: Unleash the power of large language models with the easiest and universal non-parametric pruning algorithm
[EMNLP 2024] Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning
Hierarchical Ensemble Pruning
This repository has the porpouse of give a solution to the travelling sales man problem
[CVPR 2025] "Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training" by Lexington Whalen, Zhenbang Du, Haoran You, Chaojian Li, Sixu Li, and Yingyan (Celine) Lin.
Code and trained models for our paper: N. Kaparinos, V. Mezaris, "B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM Pruning", Proc. IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW 2025).
This repository contains scripts to prune Wav2vec2 using a neuroevolution-based method. More details about this method can be found in the paper Compressing Wav2vec2 for Embedded Applications.
[PRL 2024] This is the code repo for our label-free pruning and retraining technique for autoregressive Text-VQA Transformers (TAP, TAP†).
[JCST 2023] "Inductive Lottery Ticket Learning for Graph Neural Networks" by Yongduo Sui, Xiang Wang, Tianlong Chen, Meng Wang, Xiangnan He, Tat-Seng Chua.
a C++ chess engine with support for full chess rules and an AI opponent. It implements move generation, position evaluation, and a minimax search with alpha-beta pruning to choose the best moves. This project is designed to be modular and extensible.
AI & algo-powered Checkers game. Compete against the computer!
Play the Othello board game against the algorithm and try to win!
Experiments for channel-based Structured Pruning Adapters
Official implementation of the LIES Network for Symbolic Regression
This project implements Genetic Programming (GP) to evolve and optimize mathematical expressions that best fit given data. It leverages tree-based evolutionary algorithms to generate, evaluate, and refine expressions using selection, crossover, and mutation.
Predicting employee productivity using tree models (decision tree cassification, cross validation, minimal cost-complexity pruning, random forest)
Project code developed to accompany the thesis of the bachelor programme BSc Data Science and Artificial Intelligence taught @ Universiteit Maastricht. It consists in (re-)discovering Forbidden Minors for Treewidth, through a series of graph search/analysis techniques.
An AI program that solves nonograms
A simple Tic Tac Toe Game with AI
Study and Implementation of various neural network pruning techniques. Extending the lottery ticket hypothesis to structured pruning for accelerated training while maintaining uncertainty and accuracy. The focus is on simplifying the model's complexity without sacrificing its overall performance or leading to overfitting.
NNS : Neural network surgery | academic assignment
Java implementation of an automatic player for the Murus Gallicus game.
A comprehensive implementation of post-training pruning methods for large language models (LLMs)
Machine Learning Algorithms
Dimensionality reduction of AI models for image processing through model pruning, featuring APOZ and Taylor pruning techniques.