[NeurIPS 2025] This repo is official PyTorch implementation of the paper "Learning Dense Hand Contact Estimation from Imbalanced Data".

Code for paper "Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation (ECCV 2024)"

pytorch implementation of Shrinkage loss in our ECCV paper 2018: Deep regression tracking with shrinkage loss

Deep Regression Tracking with Shrinkage Loss (ECCV 2018).

ECG Arrhythmia Detection with ResNet and Transfer Learning

The Mulan Framework with Multi-Label Resampling Algorithms

compare the performance of cross entropy, focal loss, and dice loss in solving the problem of data imbalance

software vulnerability detection

Demonstrate the application of machine learning on a real-world predictive maintenance dataset, using measurements from actual industrial equipment.

Submission for HR Analytics Hackathon - AnalysticsVidya.

Applied undersampling and oversampling using SMOTE.

dau is a Python package that implements Density-Aware Undersampling (DAU), a novel undersampling technique for handling imbalanced datasets.

Preprocessing the heart disease dataset: A practical guide to EDA, feature encoding, discretization, and handling class imbalance with SMOTE.

Customer Retention Analysis : Predict customer churn

Predicting the churn in the last month using the data (features) from the first three months and identify customers at high risk of churn and the main indicators of churn.

This notebook is a study of the application of sklearn Logistic Regression model and analysis of metric quality with a focus on the impact of imbalanced data. The problem presented is the analysis of sales of newspapers of a local stand in order to classify the probability of the newspaper being Sold Out or Not, given a set of features.

Dice loss for data-imbalanced NLP tasks

This repository features a machine learning project utilizing the Pima Indians Diabetes Dataset to predict diabetes risk. It explores data preprocessing, model training, and evaluation using techniques such as Naive Bayes and K-Nearest Neighbors (KNN) . The aim is to highlight the impact of various health factors on diabetes prediction.

This project detects failure of machine. It also detects the type of failure and gives instructions to machine operator in simple language using report.

Detección de cardiopatías en pacientes mediante el uso de datos clínicos utilizando técnicas de Machine Learning y Deep Learning.