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🥇
No-Caffeine-No-Gain's Deep Knowledge Tracing (DKT)
🧪categorical tabnet research part🧪
🏆신용카드 사용자 연체 예측 AI 경진대회 2등 솔루션🏆
This study proposes a diverse family of additive models that extend Generalized Additive Models to achieve both interpretability and predictive performance in forecasting. While preserving the stability of traditional GAMs, we develop Feature-wise Additive Models, Gradient Boosting Additive Models, and TabNet-based Neural Additive Models (TabNAM).
Master Thesis in AI focusing on TabNet in Drug Discovery
Collection of team projects from grad school
TCGA breast cancer PAM50 subtype classification pipeline with biomarker clinical validation — TCGAbiolinks/DESeq2, XGBoost/TabNet/Multi-task, SHAP stability analysis, KM/Cox survival, CORAL domain adaptation, Knowledge Graph
Leakage-aware NAM, TabNet, LightGBM, and residual MLP benchmark for APE prediction
A deep-learning approach to network intrusion detection on Wi-Fi traffic, evaluating how feature selection and class balancing techniques affect TabNet's performance on a heavily imbalanced, high-dimensional dataset.
Interactive ML Playground — sklearn / XGBoost / LightGBM / CatBoost / TabNet / neural-trees with side-by-side comparisons and 5x2cv significance tests.
Comprehensive project for Fundamentals of Artificial Intelligence, Sophomore Spring Semester. Includes classic ML algorithms (SVM, Random Forest, KNN, Gradient Boosting, Decision Tree) and two DL algorithms (Deep MLP, TabNet).
Retail customer churn prediction using PyTorch MLP and TabNet — 200k synthetic customers, RFM features, SHAP explainability, comparison against BQML baseline
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