Code for the paper The Other Side of Compression: Measuring Bias in Pruned Transformers (IDA23)

Architecture for pruning methods analysis using pytorch prune module

Analyze INN Hotels data to find which factors have a high influence on booking cancellations, build a predictive model to predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

analysing Model Pruning and Unit Pruning on a large dense MNIST network

A Portuguese hotel group seeks to understand reasons for its excessive cancellation rates.

It contains different types of machine learning models like Linear Regression, Logistic Regression, Clustering techniques, Time Series Forecasting etc

Pruning conv neural network by decreasing of num_filters in Resnet20

Official implementation of Pruning from Scratch via Pruning Module and Nuclear norm-based Regularization (WACV 2024).

Analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

A DIY text-editor with autocomplete and spell checking feature.

Code for “Discrimination-aware-Channel-Pruning-for-Deep-Neural-Networks”

Pruning, MTL, MOO benchmark repository

This project involves creating, in the language of your choice, a Gomoku game integrating an AI player capable of beating a human player the fastest way possible. To do this, you will implement a min-max algorithm but also do research, trial and error to find the most adapted heuristics. This will not be as easy as checkers.

A Backgammon bot which calculates its move using expectiminimax algorithm along with *-alpha-beta pruning.

A tutorial on how to prune the embedding layer of a language model and crafting a suitable tokenizer

ID3 decision tree implementation

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://nervanasystems.github.io/distiller

A comparative evaluation of pruning techniques for Artificial Neural Networks

C language Course Design | Tongji University

Quantifying distribution of network motifs of pruned neural networks trained to model insect flight.