arXiv:2606. 19850v1 Announce Type: cross Abstract: Deep neural networks (DNNs) exhibit attractive performance in various fields but often suffer from low interpretability.
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arXiv:2505. 08610v2 Announce Type: replace-cross Abstract: Nowadays, Neural Networks are considered one of the most effective methods for various tasks such as anomaly detection, computer-aided disease detection, or natural language processing.
arXiv:2606. 06861v1 Announce Type: cross Abstract: Understanding nonlinear feature interactions is crucial in science and engineering, yet standard multilayer perceptrons (MLPs) often capture such interactions only implicitly, leading to entangled representations that can impair robustness and interpretability.
arXiv:2607. 10077v1 Announce Type: new Abstract: Tabular learning is still dominated by gradient-boosted decision trees (GBDTs), while recent deep learning approaches have become increasingly competitive.
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.
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain incomplete. From a statistical viewpoint, a natural question is: can DNNs attain feature-learning and prediction consistency comparable to that of classical models?
arXiv:2310.16295v2 Announce Type: replace-cross Abstract: Neural network have achieved remarkable successes in many scientific fields. However, the interpretability of the neural network model is sti...