arXiv:2608. 13513v1 Announce Type: cross Abstract: Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers.
Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers. They convert tabular data into image representations, mapping each feature at a fixed pixel location derived from a dimensionality-reduction method (e.
VG‑TIE is a tabular‑to‑image encoding method that uses Natural and Horizontal Visibility Graphs to map feature values into a two‑dimensional image via PCA. Each pixel represents a feature, its intensity shows deviation from the population mean, and edges encode visibility relationships. The method offers model‑agnostic, intrinsically interpretable images, providing feature ranking through node degree distributions and local/global importance via pixel intensity combined with Grad‑CAM, and demonstrates competitive performance on six public datasets.
arXiv:2608. 03557v1 Announce Type: cross Abstract: Tabular-to-image methods that convert tabular data into visual representations have emerged as a novel paradigm for leveraging the high performance of deep learning models.
arXiv:2606. 11682v1 Announce Type: cross Abstract: Tabular-image multimodal learning aims to improve predictive modeling by jointly using structured tabular attributes and visual data.
arXiv:2605.05556v2 Announce Type: replace Abstract: Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that...
arXiv:2603. 25157v3 Announce Type: replace-cross Abstract: Recent vision backbones, such as Transformer families and state-space models like Mamba, have achieved remarkable progress on image recognition.
arXiv:2511. 12723v2 Announce Type: replace Abstract: Deep neural networks typically rely on the representation produced by their final hidden layer to make predictions, implicitly assuming that this single vector fully captures the semantics encoded across all preceding transformations.
arXiv:2607. 22872v1 Announce Type: new Abstract: Post-training quantization (PTQ) has become a practical solution for deploying deep learning models on resource-constrained edge devices by compressing high-precision floating-point weights into low-precision representations without requiring retraining.
The paper introduces an importance‑scoring metric for multi‑head transformer attention heads applied to tabular data, a domain where transformers have been less studied. Experiments on 40 diverse tabular datasets show that removing heads with the lowest importance scores has minimal impact on performance, while removing the most important head first causes the largest drop. The study finds that important heads are distributed across layers and vary significantly across different tabular schemas, suggesting that the proposed score can help reduce redundancy and improve transformer efficiency.
arXiv:2607. 22139v1 Announce Type: cross Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols.
arXiv:2604. 00086v2 Announce Type: replace-cross Abstract: The field of computer vision has experienced significant advancements through scalable vision encoders and multimodal pre-training frameworks.
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.