PyTorch Tabular: A Framework for Deep Learning with Tabular Data

28 April 2021

Papers citing "PyTorch Tabular: A Framework for Deep Learning with Tabular Data"

18 / 18 papers shown

Title
Representation Learning for Tabular Data: A Comprehensive Survey Jun-Peng Jiang Si-Yang Liu Hao-Run Cai Qile Zhou Han-Jia Ye LMTD 46 0 0 17 Apr 2025
A Survey on Deep Tabular Learning Shriyank Somvanshi Subasish Das Syed Aaqib Javed Gian Antariksa Ahmed Hossain LMTD 34 7 0 15 Oct 2024
Groningen: Spatial Prediction of Rock Gas Saturation by Leveraging Selected and Augmented Well and Seismic Data with Classifier Ensembles Dmitry Ivlev 36 0 0 14 Oct 2024
MonoSparse-CAM: Harnessing Monotonicity and Sparsity for Enhanced Tree Model Processing on CAMs Tergel Molom-Ochir Brady Taylor Hai Li Yiran Chen 16 0 0 12 Jul 2024
Fault Detection for agents on power grid topology optimization: A Comprehensive analysis Malte Lehna Mohamed Hassouna Dmitry Degtyar Sven Tomforde Christoph Scholz AI4CE 28 2 0 24 Jun 2024
ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability Yanming Guo Charles Guan Jin Ma 24 2 0 11 Jun 2024
A systematic study comparing hyperparameter optimization engines on tabular data Balazs Kegl 21 1 0 27 Nov 2023
CAST: Cluster-Aware Self-Training for Tabular Data Minwook Kim Juseong Kim Kibeom Kim Giltae Song 33 0 0 10 Oct 2023
Bidirectional Attention as a Mixture of Continuous Word Experts Kevin Christian Wibisono Yixin Wang MoE 13 0 0 08 Jul 2023
XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patients Munib Mesinovic Peter Watkinson Ting Zhu 22 3 0 10 May 2023
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time Huaxiu Yao Caroline Choi Bochuan Cao Yoonho Lee Pang Wei Koh Chelsea Finn OOD 19 73 0 25 Nov 2022
GANDALF: Gated Adaptive Network for Deep Automated Learning of Features Manu Joseph Harsh Raj 23 9 0 18 Jul 2022
Interpretable Mixture of Experts Aya Abdelsalam Ismail Sercan Ö. Arik Jinsung Yoon Ankur Taly S. Feizi Tomas Pfister MoE 20 10 0 05 Jun 2022
COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data Hossein Aboutalebi Maya Pavlova M. Shafiee A. Florea Andrew Hryniowski Alexander Wong 22 4 0 24 Apr 2022
Deep Neural Networks and Tabular Data: A Survey V. Borisov Tobias Leemann Kathrin Seßler Johannes Haug Martin Pawelczyk Gjergji Kasneci LMTD 27 646 0 05 Oct 2021
Muddling Label Regularization: Deep Learning for Tabular Datasets Karim Lounici Katia Méziani Benjamin Riu 16 6 0 08 Jun 2021
TrustyAI Explainability Toolkit Rob Geada Tommaso Teofili Rui Vieira Rebecca Whitworth Daniele Zonca 16 2 0 26 Apr 2021
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data Sergei Popov S. Morozov Artem Babenko LMTD 85 294 0 13 Sep 2019