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arXiv Machine Learning By Xiao Zhao, Daniela Oelke

Solving In-Table Prediction Problems by Deep Neural Networks with Performance Evaluation Using Synthetic Data

Read the original on arXiv Machine Learning →

The paper introduces In-Table Prediction (ITB), a self‑supervised task where deep neural networks learn to predict any column in a table from the remaining columns. It proposes a novel neural layer to handle missing continuous values, generates synthetic datasets with controlled column relationships, and evaluates three architectures—MLP, ResNet, and Transformer—showing that attention‑based Transformers perform best when ample training data and large embeddings are used. The study is limited to synthetic, small‑column tables and is presented as an initial investigation rather than a comprehensive real‑world analysis.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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