{"total":18,"rows":[{"id":"2404.08978","title":"Incremental Residual Concept Bottleneck Models","created":"2024-04-13","cited_by_count":43,"area":null},{"id":"2309.07742","title":"Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning","created":"2023-09-14","cited_by_count":22,"area":null},{"id":"2406.10368","title":"A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcuts","created":"2024-06-14","cited_by_count":19,"area":null},{"id":"2409.13456","title":"Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?","created":"2024-09-20","cited_by_count":18,"area":null},{"id":"2504.19774","title":"If Concept Bottlenecks are the Question, are Foundation Models the Answer?","created":"2025-04-28","cited_by_count":15,"area":null},{"id":"2310.11884","title":"From Neural Activations to Concepts: A Survey on Explaining Concepts in Neural Networks","created":"2023-10-18","cited_by_count":13,"area":null},{"id":"2502.11245","title":"Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens","created":"2025-02-16","cited_by_count":13,"area":null},{"id":"2304.14864","title":"Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability","created":"2023-04-28","cited_by_count":11,"area":null},{"id":"2311.15303","title":"Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement","created":"2023-11-26","cited_by_count":10,"area":null},{"id":"2311.14435","title":"Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces","created":"2023-11-24","cited_by_count":7,"area":null},{"id":"2308.15096","title":"How Faithful are Self-Explainable GNNs?","created":"2023-08-29","cited_by_count":5,"area":null},{"id":"2305.07663","title":"Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces","created":"2023-04-30","cited_by_count":4,"area":null},{"id":"2404.08964","title":"Understanding Multimodal Deep Neural Networks: A Concept Selection View","created":"2024-04-13","cited_by_count":4,"area":null},{"id":"2503.18483","title":"Explaining Domain Shifts in Language: Concept erasing for Interpretable Image Classification","created":"2025-03-24","cited_by_count":2,"area":null},{"id":"2409.17109","title":"Unveiling Ontological Commitment in Multi-Modal Foundation Models","created":"2024-09-25","cited_by_count":1,"area":null},{"id":"2504.08602","title":"On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNs","created":"2025-04-11","cited_by_count":1,"area":null},{"id":"2306.03531","title":"A Unified Concept-Based System for Local, Global, and Misclassification Explanations","created":"2023-06-06","cited_by_count":0,"area":null},{"id":"2404.02830","title":"Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes","created":"2024-04-03","cited_by_count":0,"area":null}],"next_cursor":null}