Automation of Tabular Application Formation by S. V. Zykin

Subjects: “…tabular applications…”
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Transforming tabular data into images via enhanced spatial relationships for CNN processing by Hameedah A. Alenizy, Jawad Berri

“…This paper introduces an original approach that transforms tabular data into a format suitable for CNN processing. …”
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Research on Fine-Tuning Optimization Strategies for Large Language Models in Tabular Data Processing by Xiaoyong Zhao, Xingxin Leng, Lei Wang, Ningning Wang

“…Despite their impressive performance across various language tasks, these models still encounter challenges when processing tabular data. This study investigates the optimization of fine-tuning strategies for LLMs specifically in the context of tabular data processing. …”
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Advanced solar radiation prediction using combined satellite imagery and tabular data processing by Mohammed Attya, O. M. Abo-Seida, H. M. Abdulkader, Amgad M. Mohammed

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SynC2S: An Efficient Method for Synthesizing Tabular Data With a Learnable Pre-Processing by Jiwoo Kim, Seri Park, Junsung Koh, Dongha Kim

“…In this study, we propose an efficient and theoretically principled method based on a deep generative model to effectively generate high-quality synthetic tabular data. First, we introduce a novel technique called C2Smap–a learnable pre-processing method that automatically transforms continuous distributions into simpler and easily generatable forms. …”
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Towards automated detection of adversarial attacks on tabular data by Piotr Biczyk, Łukasz Wawrowski

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Investigating imperceptibility of adversarial attacks on tabular data: An empirical analysis by Zhipeng He, Chun Ouyang, Laith Alzubaidi, Alistair Barros, Catarina Moreira

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Scoring System for Quantifying the Privacy in Re-Identification of Tabular Datasets by Jakob Folz, Manjitha D. Vidanalage, Robert Aufschlager, Amar Almaini, Michael Heigl, Dalibor Fiala, Martin Schramm

“…However, correlation-only and uniformity-only risk required significantly more processing time, while Markov Model risk incurred the highest computational cost. …”
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DEVELOPMENT OF TABULAR METHOD FOR CALCULATION OF PRIMITIVE FUNCTION IN MODULAR CODE by Vitaly V. Grankin

“…The item dwells on the solution for tabular calculation of primitive function in modular code, both in view of hardware costs and in view of the processing speed.…”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The proposed study is founded on two principal pillars: the transformation of process tabular data (generated using the Conditional Tabular Generative Adversarial Network (CTGAN)), involving the mapping of features onto a fixed grid in a heatmap structure, and the configuration of the CNN algorithm to extract complex patterns in the data that are not readily apparent in the original tabular format. …”
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A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data by Prativa Pokhrel, Alina Lazar

“…The performance of machine learning (ML) methods for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values. …”
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Enhancing RAG Performance by Representing Hierarchical Nodes in Headers for Tabular Data by Minchae Song

“…This study introduced hierarchical node-based header representation models designed to enhance the processing and integration of external table data, addressing the key limitations of existing RAG techniques when working with structured table sources. …”
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Counterfactual and Prototypical Explanations for Tabular Data via Interpretable Latent Space by Simone Piaggesi, Francesco Bodria, Riccardo Guidotti, Fosca Giannotti, Dino Pedreschi

“…In this paper, we propose CP-ILS, a comprehensive interpretable feature reduction method for tabular data capable of generating Counterfactual and Prototypical post-hoc explanations using an Interpretable Latent Space. …”
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GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets by Ehsan Namjoo, Alison N. O’Connor, Jim Buckley, Conor Ryan

“…This paper introduces a novel XAI system designed for classification tasks on tabular data, which offers a balance between performance and interpretability. …”
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Bridging the Gap Between Tabular Information Requirements and the Information Delivery Specification (IDS) by Simon Fischer, Harald Urban, Christian Schranz, Gerhard Zucker

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A systematic review of privacy-preserving techniques for synthetic tabular health data by Tobias Hyrup, Anton D. Lautrup, Arthur Zimek, Peter Schneider-Kamp

“…This review aims at uncovering how well privacy is preserved in tabular health data for different SDG methods and how privacy can be explicitly implemented in the SDG process. …”
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Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning by Carlos Huertas

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TabMoE: A General Framework for Diverse Table-Based Reasoning with Mixture-of-Experts by Jie Wu, Mengshu Hou

Subjects: “…tabular data…”
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Rule-based approximation of black-box classifiers for tabular data to generate global and local explanations by Cezary Maszczyk, Michal Kozielski, Marek Sikora

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LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer by Qin Lin, Jie Sheng, Chang Zhou, Tao Xiao, Yilei Meng, Mingxin Lu, Junfang Zhang, Xueyun Yan, Luying Peng, Huaming Cao, Li Li

“…Long-noncoding RNAs (LncRNAs) play important roles in physiological and pathological processes. Accurately predicting lncRNA-protein interactions (LPIs) is vital strategy for clarify functions and pathogenic mechanisms of lncRNAs. …”
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