The paper introduces weighted t‑SNE, a variant of t‑SNE that incorporates feature importance scores from feature scorers into the 2‑D embedding of high‑dimensional data. It explains how to implement the method, proposes using the silhouette coefficient to evaluate the resulting visualizations, and demonstrates its use on synthetic and real‑world tabular datasets with nine different feature scorers. The approach allows practitioners to compare feature scorers visually and to interpret how machine‑learning models organize data based on learned feature importance.
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.
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.
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...
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.
The paper introduces Ra‑NEM, a method that optimizes the area under insertion and deletion curves to improve the faithfulness of feature importance attributions in machine learning models. By deriving an objective function from faithfulness and approximating its gradient, the authors link insertion curves to top‑k feature selection, enabling a loss function that measures attribution quality. Ra‑NEM, integrated with the neural explanation mask framework, works with any differentiable model, yielding accurate, robust, and fast attributions that outperform existing algorithms on multiple XAI metrics.
arXiv:2607. 27463v1 Announce Type: new Abstract: Dimensionality Reduction (DR) is a fundamental tool for high-dimensional data exploration, reducing the complexity of latent spaces of machine learning models, and assisting in the explanation of complex opaque models.
The paper introduces a post‑hoc Feature Selection Layer (FSL) that can be added to frozen pre‑trained neural networks on tabular data to highlight the most important features. This lightweight, trainable module fine‑tunes feature weights based on the original model’s outputs without altering its architecture or parameters. Experiments on synthetic and real‑world datasets show that the post‑hoc FSL preserves predictive performance while improving interpretability, outperforming other post‑hoc methods such as Integrated Gradients and SHAP.
arXiv:2607. 24145v1 Announce Type: new Abstract: Feature selection aims to identify the most informative and relevant features for a given dataset, either in terms of capturing the underlying data structure and distribution better, or with respect to the performance on a downstream task.
The paper introduces View distance, a novel metric that projects high‑dimensional data onto all pairwise two‑dimensional planes and sums the Euclidean distances across these projections. It satisfies metric axioms, couples features, suppresses redundancy, and captures anisotropic geometry. To make it scalable, the authors propose a plane‑selection strategy using iterative Maximum Weight Matching, reducing complexity from ω(n²) to ω(k) and demonstrating competitive performance on twelve datasets.
arXiv:2601. 13591v2 Announce Type: replace Abstract: Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning.
HierViT is a hierarchical Vision Transformer designed for medical image classification that balances strong predictive performance with interpretability. The model first predicts clinically relevant visual appearance attributes and then uses a hierarchical reasoning process to infer the final diagnosis. Attribute‑specific prototypes and attention maps are visualized to provide complementary views of the model’s reasoning, linking representative image examples and spatial attention to semantically defined visual features.
arXiv:2512. 07355v2 Announce Type: replace Abstract: Two traditions of interpretability have evolved side by side but seldom spoken to each other: Concept Bottleneck Models (CBMs), which prescribe what a concept should be, and Sparse Autoencoders (SAEs), which discover what concepts emerge.