Cover Image

XNNTab -- Interpretable Neural Networks for Tabular Data using Sparse Autoencoders

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Elhadri, Khawla, Schlötterer, Jörg, Seifert, Christin
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Interpretable Deep Neural Networks for Tabular Data
by: Elhadri, Khawla, et al.
Published: (2025)

This looks like what? Challenges and Future Research Directions for Part-Prototype Models
by: Elhadri, Khawla, et al.
Published: (2025)

An XAI-based Analysis of Shortcut Learning in Neural Networks
by: Le, Phuong Quynh, et al.
Published: (2025)

Persuasion Tokens for Editing Factual Knowledge in LLMs
by: Youssef, Paul, et al.
Published: (2026)

Invariant Learning with Annotation-free Environments
by: Le, Phuong Quynh, et al.
Published: (2025)

Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations
by: Le, Phuong Quynh, et al.
Published: (2024)

A Second Look on BASS -- Boosting Abstractive Summarization with Unified Semantic Graphs -- A Replication Study
by: Koraş, Osman Alperen, et al.
Published: (2024)

Is Last Layer Re-Training Truly Sufficient for Robustness to Spurious Correlations?
by: Le, Phuong Quynh, et al.
Published: (2023)

Shortcut Mitigation via Spurious-Positive Samples
by: Le, Phuong Quynh, et al.
Published: (2026)

Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers
by: Kuhn, Lukas, et al.
Published: (2025)

Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders
by: Marks, Luke, et al.
Published: (2024)

Interpretable Graph Neural Networks for Heterogeneous Tabular Data
by: Alkhatib, Amr, et al.
Published: (2024)

Interpretable Graph Neural Networks for Tabular Data
by: Alkhatib, Amr, et al.
Published: (2023)

Interpretable Mesomorphic Networks for Tabular Data
by: Kadra, Arlind, et al.
Published: (2023)

Mechanistic Interpretability with Sparse Autoencoder Neural Operators
by: Tolooshams, Bahareh, et al.
Published: (2025)

The Queen of England is not England's Queen: On the Lack of Factual Coherency in PLMs
by: Youssef, Paul, et al.
Published: (2024)

Enhancing Fact Retrieval in PLMs through Truthfulness
by: Youssef, Paul, et al.
Published: (2024)

Transcoders Beat Sparse Autoencoders for Interpretability
by: Paulo, Gonçalo, et al.
Published: (2025)

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit
by: Jiang, Nick, et al.
Published: (2025)

Interpretable Reward Model via Sparse Autoencoder
by: Zhang, Shuyi, et al.
Published: (2025)

Interpreting Attention Layer Outputs with Sparse Autoencoders
by: Kissane, Connor, et al.
Published: (2024)

Posterior-Calibrated Causal Circuits in Variational Autoencoders: Why Image-Domain Interpretability Fails on Tabular Data
by: Roy, Dip, et al.
Published: (2026)

Route Sparse Autoencoder to Interpret Large Language Models
by: Shi, Wei, et al.
Published: (2025)

Step-Level Sparse Autoencoder for Reasoning Process Interpretation
by: Yang, Xuan, et al.
Published: (2026)

Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control
by: Makelov, Aleksandar, et al.
Published: (2024)

Guiding LLMs to Generate High-Fidelity and High-Quality Counterfactual Explanations for Text Classification
by: Nguyen, Van Bach, et al.
Published: (2025)

From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent
by: Nguyen, Van Bach, et al.
Published: (2022)

CEval: A Benchmark for Evaluating Counterfactual Text Generation
by: Nguyen, Van Bach, et al.
Published: (2024)

Interpretable Deep Clustering for Tabular Data
by: Svirsky, Jonathan, et al.
Published: (2023)

Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
by: Sattarov, Timur, et al.
Published: (2025)

Group Equivariance Meets Mechanistic Interpretability: Equivariant Sparse Autoencoders
by: Erdogan, Ege, et al.
Published: (2025)

Transformer Key-Value Memories Are Nearly as Interpretable as Sparse Autoencoders
by: Ye, Mengyu, et al.
Published: (2025)

Interpretable Company Similarity with Sparse Autoencoders
by: Molinari, Marco, et al.
Published: (2024)

CardiCat: a Variational Autoencoder for High-Cardinality Tabular Data
by: Carlin, Lee, et al.
Published: (2025)

Kronecker Factorization Improves Efficiency and Interpretability of Sparse Autoencoders
by: Kurochkin, Vadim, et al.
Published: (2025)

Interpreting and Steering Protein Language Models through Sparse Autoencoders
by: Garcia, Edith Natalia Villegas, et al.
Published: (2025)

Interpreting CLIP with Hierarchical Sparse Autoencoders
by: Zaigrajew, Vladimir, et al.
Published: (2025)

IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage
by: Li, Yuhan, et al.
Published: (2026)

Interpreting CFD Surrogates through Sparse Autoencoders
by: Hu, Yeping, et al.
Published: (2025)

Interpretable and Steerable Concept Bottleneck Sparse Autoencoders
by: Kulkarni, Akshay, et al.
Published: (2025)