There are 3 repositories under tabnet topic.
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
Modification of TabNet as suggested in the Medium article, "The Unreasonable Ineffectiveness of Deep Learning on Tabular Data"
🥇KNOW기반 직업 추천 알고리즘 경진대회 1등 솔루션입니다🥇
Real-time aircraft localization prediction based on crowdsourced air traffic control communication data (ADS-B)
No-Caffeine-No-Gain's Deep Knowledge Tracing (DKT)
Kaggle Competition
🏆신용카드 사용자 연체 예측 AI 경진대회 2등 솔루션🏆
🧪categorical tabnet research part🧪
This project has applied Machine Learning and Deep Learning techniques to analyse and predict the Air Quality in Beijing.
image transformation and enhancement based attacks on fingerprint presentation attack detection systems
TabNet: Attentive Interpretable Tabular Learning (Pytorch implementation)
使用比赛方提供的脱敏数据,进行客户信贷流失预测。
Predict fraud transaction
The aim of this project is to experiment with various machine learning models that predict whether or not a patient will show up for a scheduled appointment. The project includes data processing and analysis. Also explainable AI methods are incorporated.
데이콘, 음향 데이터 COVID-19 검출 AI 경진대회
📊 A comprehensive comparison of TabNet and XGBoost across binary classification, multiclass classification, and regression tasks, showcasing performance metrics and fine-tuning results.
This is the solution for stock-market prediction problem given in flipr 5.0.
A methodology development using tabnet transformers for car insurance prediction.
Regression task using techniques of Machine Learning, Deep Learning and Transformers
this project utilizes difference deep-learning algorithms to detect fraud operations on banking systems ( classification problem )
Model to decide whether someone will or will not accept coupons from tabular data.
Project developed for the Data Analytics course of the UniBO Master's Degree
Tabular Data Processing lightgbm, tabnet, resnet
Predicting writing quality based on data statistics of the writing process. The key lies in feature engineering and tree models.
Classifying Travel Mode choice in the Netherlands using KNN, XGBoost, RF and TabNet
We will conduct a comprehensive analysis of the dataset, focusing on identifying key features that influence outcomes. To achieve this, we will employ Logistic Regression and TabNet models to discern feature importance.
Solar Flare Prediction Using Deep Learning
A collection of team projects