Concept-Based Explainability

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A Hilbert-Valued Functional Decomposition Framework for Explaining Time-Dependent Outputs
Sep 10, 2026Sophie Hanna Langbein, Niklas Koenen, Marvin N. Wright +1Concept-Based Explainability Energy Forecasting
"World Knowledge" in the Weights: Reading Concept Circuits of Vision Transformers
Sep 8, 2026Yanlin Chen, Tang Li, Xi PengVision Transformer Concept-Based Explainability
A Quantitative Evaluation Framework for Temporal Explainability in Echocardiographic Video Segmentation
Sep 7, 2026Jiyoo Noh, Jonathan H. ChanConcept-Based Explainability U-Net
When Superpixels Fail on Documents: A Study of Segmentation for LIME Explanations
Sep 7, 2026Quentin Telnoff, Emanuela Boros, Mickaël Coustaty +3Concept-Based Explainability Document
Witnesses Explain Anomalies
Sep 3, 2026Lamine DiopAnomaly Score Anomaly
ICON Decomposition: Auditing deep neural networks for shortcuts by decomposing layer-wise representations using concepts
Aug 26, 2026Roshan Prakash Rane, Marco Simnacher, Manuel Pfeuffer +7Concept-Based Explainability Deep Neural Network
Class Activation Mapping in Explainable Computer Vision: A Method-Centered Review of CNN, Transformer, and Foundation-Model-Era Visual Explanations
Aug 12, 2026AmirHossein Eshghi, Hamid Saadatfar, Seyyed Ali Hoseini +2Concept-Based Explainability Explainable Artificial Intelligence
SAGE: Semantic Explainability of Attention-Based Survival Models in Computational Pathology
Aug 3, 2026Abdallah Lamane, Abdul Rahman Diab, Ren-Chin Wu +1Pathology Foundation Models Multiple Instance Learning
Contrastive Concept Importance: Explaining Pairwise Class Decisions Through Automatically Extracted Concept Representations
Jul 30, 2026Roel Visser, Isaac Roberts, Barbara HammerContrastive Concept-Based Explainability
FADEx: Feature Attribution and Distortion-based Explanation of Dimensionality Reduction
Jul 29, 2026Lucas Greff Meneses, Evandro S. Ortigossa, Claudio Silva +1Dimensionality Reduction Concept-Based Explainability
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations
Jul 28, 2026Neta Kirmayer, David Tayouri, Andrés Murillo +3Cybersecurity Graph Anomaly Detection
KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability
Jul 27, 2026Krithi Shailya, Ananya Lakshmi Ravi, Venkatanathan K. V. +4Concept-Based Explainability Medical Vision-Language Models
Behavior-Driven Explainability
Jul 27, 2026Caroline Dominik, Rolf DrechslerConcept-Based Explainability Explainability
Explaining BiomedCLIP with Weighted Banzhaf Interactions Supported by Tree-Gram Parsing
Jul 25, 2026Jakub Rymarski, Adam Rempała, Bartłomiej Sobieski +1Concept-Based Explainability Medical Vision-Language Models
Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls
Jul 23, 2026Jiancu Chen, Shuyin Xia, Guan Wang +2Graph Neural Networks Subgraphs
Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification
Jul 23, 2026Kritanu Chattopadhyay, Sayanjit Singha Roy, Soumya ChatterjeeRetinal Imaging Counterfactual Explanation
Attention Without Grounding: Causal Evaluation of Visual Explanations in Medical VLMs
Jul 20, 2026Binesh Sadanandan, Vahid BehzadanMedical Vision-Language Models Concept-Based Explainability
Cross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoders
Jul 9, 2026Bendegúz Váradi, Zoltán KmettyImproving Sparse Autoencoders Sparse Autoencoders
Naming the Concepts Classifiers Rely On: Language-Anchored Decomposition for Faithful Explanation
Jul 8, 2026Ahsan Habib Akash, Dipkamal Bhusal, Stacey Jones +3Concept-Based Explainability Concept Bottleneck Models
Why Fake ? Unveiling the Semantic Vocabulary of Deepfake Detectors
Jul 8, 2026Vazgken Vanian, Alexandros Doumanoglou, Dimitris ZarpalasDeepfake Detection Unsupervised Detectors
ReMoDEx: A Local-to-Global Relevance-Based Model Decision Explainability Framework for large-Scale Image Datasets
Jul 8, 2026Abhay Kumar Pathak, Mrityunjay Chaubey, Manjari GuptaConcept-Based Explainability Image Classification
Beyond Heatmaps: Unsupervised Concept-Graph Reasoning for Interpretable Visual Explanation
Jul 1, 2026Md Mohasin Hossain, Anar Amirli, Robert Leist +2Class-Incremental Concept Bottleneck Model Concept-Based Explainability
Low-cost concept-based localized explanations: How far can we get with training-free approaches?
Jun 27, 2026Darian Fernández-Gutiérrez, Rafael Bello, Marilyn Bello +1Concept-Based Explainability Explainable Artificial Intelligence
AnTenA: Actionable and Explainable Tensor Analysis System with Large Language Models
Jun 27, 2026Dawon Ahn, Auder Der, Evangelos E. PapalexakisConcept-Based Explainability Explanations
Towards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment
Jun 19, 2026Xiaoliang Wu, Qiyang Sun, Yupei Li +3Concept-Based Explainability Xai
CIExplainer++: Generating Causal and Interpretable Explanations for Graph Neural Networks
Jun 17, 2026Francisco Caldas, Sahil Satish Kumar, Ruben Belo +1Concept-Based Explainability Explainable Artificial Intelligence
Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing
Jun 16, 2026Kexin Chen, Yi Liu, Haonan Zhang +3Deception Concept-Based Explainability
Phonetically Explainable Speech Deepfake Detection
Jun 13, 2026Manasi Chhibber, Jagabandhu Mishra, Tomi H. KinnunenAudio Deepfake Detection Spoofed Speech
What Do Deepfake Speech Detectors Actually Hear?
Jun 9, 2026Vojtěch Staněk, Veronika Jirmusová, Anton Firc +3Audio Deepfake Detection Unsupervised Detectors
Sample-Efficient LLM-Based Detection of Malicious Web Server Logs with Forensically Explainable Reasoning
Jun 7, 2026Bernhard Kneip, Nhien-An Le-Khac, Hong-Hanh Nguyen-LeLog Digital Forensics
TLRD: Teaching LLMs to Reason over Tabular Data with Tri-Level Rationale Distillation
Jun 6, 2026Tianyuan Liang, Xuwei Tan, Lei Shi +6LLM Reasoning Methods Tabular Data
OPTIMUS-Prime: Minimal and Sufficient Concept Explanations for Deep Vision Models
Jun 5, 2026Arthur Hoarau, Chenrui Zhu, Vu Linh NguyenConcept-Based Explainability Explainable Artificial Intelligence
LLM Explainability with Counterfactual Chains and Causal Graphs
Jun 4, 2026Nirit Nussbaum-Hoffer, Nitay Calderon, Liat Ein-Dor +1Causal Graph Concept-Based Explainability
EIVE: End-to-End Instance-Specific Visual Explanations for Detection Transformers
Jun 1, 2026Jianlin Xiang, Yanshan Li, Linhui DaiObject Detection Concept-Based Explainability
Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures
May 30, 2026Ping Xiong, Thomas Schnake, Grégoire Montavon +2Concept-Based Explainability Artificial Neural Networks
Density-Guided Robust Counterfactual Explanations on Tabular Data under Model Multiplicity
May 29, 2026Jun Tan, Qing Guo, Zicheng Xu +3Counterfactual Explanation Randomized Global-Local Density Estimation
Improving Adversarial Robustness of Attribution via Implicit Regularization
May 28, 2026Amir Mehrpanah, Matteo Gamba, Hossein AzizpourAdversarial Transferability Concept-Based Explainability
CB-SLICE: Concept-Based Interpretable Error Slice Discovery
May 28, 2026Yael Konforti, Mateo Espinosa Zarlenga, Elaf Almahmoud +1Concept-Based Explainability Concept Bottleneck Models
Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
May 27, 2026Tianyang Zhou, Wenbo Chen, Pierre Jinghong Liang +1Concept-Based Explainability Automatic Prompt Optimization
Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection
May 27, 2026Antonia Šarčević, Nikolina FridElectroencephalogram Depression
A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks
May 27, 2026Nicolas Tremblay, Benjamin Ricaud, Filippo Maria BianchiGraph Neural Networks Concept-Based Explainability
Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers
May 27, 2026Carmen Quiles-Ramírez, Leticia L. Rodríguez, Nicolás Martorell +1Concept-Based Explainability Large Multimodal Models
Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
May 26, 2026Tomás Pereira, João Vitorino, Eva Maia +1Explainability Concept-Based Explainability
Universal Activation Verbalizer: A Unified Framework for Cross-Model Activation Explanation
May 25, 2026Haiyan Zhao, Zirui He, Guanchu Wang +3Verbalization Model Activations
HeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detection
May 23, 2026Shubham Gupta, Nikhil Panwar, Partha Pratim RoyElectrocardiogram Image Classification Electrocardiogram
Hoeffding Concept Bottleneck Models with Applications to Overhead Images
May 22, 2026Clément Bénard, Manon Arfib, Christophe Labreuche +1Class-Incremental Concept Bottleneck Model Concept Bottleneck Models
Towards Explainability of SLMs by investigating Token Level Activation
May 21, 2026Sayantani Ghosh, Rajashik Datta, Amit Kumar Das +1Model Activations Token-Level Uncertainty
SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection
May 20, 2026Wanying Tan, Shuo Yan, Dazhi Huang +7Concept-Based Explainability Bacterial Colony Counting
A Framework for Evaluating Zero-Shot Image Generation in Concept-based Explainability
May 19, 2026Giacomo Astolfi, Matteo Bianchi, Riccardo Campi +2Explainable Artificial Intelligence Xai
OCCAM: Open-set Causal Concept explAnation and Ontology induction for black-box vision Models
May 18, 2026Chiara Maria Russo, Simone Carnemolla, Simone Palazzo +3Concept-Based Explainability Vision Foundation Models
Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations
May 18, 2026Baptiste Ferrere, Nicolas Bousquet, Fabrice Gamboa +1Concept-Based Explainability Generalized Linear Model
UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models
May 17, 2026Hyunju Kang, Geonhee Han, Hogun ParkCounterfactual Explanation Graph Learning
Zero-Shot Faithful Textual Explanations via Directional-Derivative Influence on Predictions
May 16, 2026Toshinori Yamauchi, Hiroshi Kera, Kazuhiko KawamotoConcept-Based Explainability Zero-Shot Benchmark
Right Predictions, Misleading Explanations: On the Vulnerability of Vision-Language Model Explanations
May 15, 2026Narges Babadi, Hadis KarimipourBlack-Box Adversarial Attacks Concept-Based Explainability
Toward Template-Free Explainability for Monte Carlo Tree Search
May 15, 2026Siqi Lu, Mirsaleh Bahavarnia, Hiba Baroud +3Monte Carlo Tree Search Concept-Based Explainability
$α$ -TCAV: A Unified Framework for Testing with Concept Activation Vectorsα-TCAV: A Unified Framework for Testing with Concept Activation Vectors
May 15, 2026Ekkehard Schnoor, Jawher Said, Malik Tiomoko +2Concept-Based Explainability Model Activations
Architecture-Aware Explanation Auditing for Industrial Visual Inspection
May 14, 2026Sibo Jia, Zihang Zhao, Kunrong LiConcept-Based Explainability Inspection
Explanation-Aware Learning for Enhanced Interpretability in Biomedical Imaging
May 11, 2026Zubair Faruqui, Rahul DubeyMedical Imaging Datasets Concept-Based Explainability
Hierarchical Attention-based Graph Neural Network with Relevance-driven Pruning
May 10, 2026Seungwoo KumGraph Neural Networks Multi-View Gated Graph Attention Network
Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
May 7, 2026Ronaldo Canizales, Divya Gopinath, Corina Păsăreanu +1Concept-Based Explainability Explanations

Sep 10, 2026 · stat.MLJ/K move · Enter open · S save

A Hilbert-Valued Functional Decomposition Framework for Explaining Time-Dependent Outputs

Sophie Hanna Langbein, Niklas Koenen, Marvin N. Wright, Julia Herbinger

Feature-based explanations quantify features' influence on model predictions, but are primarily designed for scalar outputs. In many applications, however, outputs are functional or multivariate, such as time-dependent trajectories in demand forecasting. Consequently, existing approaches typically explain each output location independently, ignoring dependencies across the output components. We address this limitation by developing a unified framework for feature-based explanations of time-dependent outputs. Specifically, we generalize functional decomposition to Hilbert-valued prediction functions and extend an existing feature-based explanation framework to this setting. Our framework introduces kernel-based output representations that enable time-dependency-aware explanations at multiple levels of temporal granularity, including time-specific, time-resolved, and time-aggregated, while providing a unified view in which existing methods arise as special cases. We validate our framework on synthetic and real-world data, including intraday financial market volatility prediction and energy demand forecasting.

Concept-Based Explainability Energy Forecasting

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