Rethinking Data Augmentation for Tabular Data in Deep Learning

17 May 2023

Papers citing "Rethinking Data Augmentation for Tabular Data in Deep Learning"

9 / 9 papers shown

Title
Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series Ching Chang Chiao-Tung Chan Wei-Yao Wang Wen-Chih Peng Tien-Fu Chen AI4TS 22 0 0 16 Oct 2024
SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation Mucong Ding Bang An Yuancheng Xu Anirudh Satheesh Furong Huang 14 1 0 03 Oct 2024
Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey Xi Fang Weijie Xu Fiona Anting Tan Jiani Zhang Ziqing Hu Yanjun Qi Scott Nickleach Diego Socolinsky Srinivasan H. Sengamedu Christos Faloutsos LMTD ALM 30 63 0 27 Feb 2024
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification Nathan Painchaud P. Courand Pierre-Marc Jodoin Nicolas Duchateau Olivier Bernard 31 2 0 15 Jan 2024
TimeDRL: Disentangled Representation Learning for Multivariate Time-Series Ching Chang Chiao-Tung Chan Wei-Yao Wang Wenjie Peng Tien-Fu Chen AI4TS 15 2 0 07 Dec 2023
Self-supervised learning of multi-omics embeddings in the low-label, high-data regime Christian John Hurry Emma Slade 25 0 0 16 Nov 2023
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks M. Atemkeng Toheeb Aduramomi Jimoh 11 1 0 30 Sep 2023
Multimodal Data Augmentation for Image Captioning using Diffusion Models Changrong Xiao S. Xu Kunpeng Zhang DiffM 16 10 0 03 May 2023
SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla Kevin W. Bowyer Lawrence Hall W. Kegelmeyer AI4TS 160 25,150 0 09 Jun 2011