[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
Shortcut to Nowhere: Demystifying Deep Spurious Regression
compare the performance of cross entropy, focal loss, and dice loss in solving the problem of data imbalance
The Mulan Framework with Multi-Label Resampling Algorithms
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.
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.
Applied undersampling and oversampling using SMOTE.
Real-time fraud detection microservice built with XGBoost, FastAPI, and Docker, implementing an end-to-end machine learning pipeline from EDA to production deployment.
Real-time ML data drift detection system monitors model health using KS statistical testing and alerts when retraining is needed.
dau is a Python package that implements Density-Aware Undersampling (DAU), a novel undersampling technique for handling imbalanced datasets.
A machine learning solution for churn prediction using CatBoost, achieving a 0.8464 AUC-ROC through feature engineering and hyperparameter optimization.
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
Codes for paper titled "TC-Sniffer: A Transformer-CNN Bibranch Framework Leveraging Auxiliary VOCs for Few-Shot UBC Diagnosis via Electronic Noses"
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