{"total":61,"rows":[{"id":"2304.06129","title":"Label-Free Concept Bottleneck Models","created":"2023-04-12","cited_by_count":285,"area":null},{"id":"2310.13018","title":"Getting aligned on representational alignment","created":"2023-10-18","cited_by_count":143,"area":null},{"id":"2310.19775","title":"Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions","created":"2023-10-30","cited_by_count":125,"area":null},{"id":"2306.01574","title":"Probabilistic Concept Bottleneck Models","created":"2023-06-02","cited_by_count":101,"area":null},{"id":"2302.14260","title":"A Closer Look at the Intervention Procedure of Concept Bottleneck Models","created":"2023-02-28","cited_by_count":64,"area":null},{"id":"2305.19951","title":"Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts","created":"2023-05-31","cited_by_count":63,"area":null},{"id":"2304.14068","title":"Interpretable Neural-Symbolic Concept Reasoning","created":"2023-04-27","cited_by_count":57,"area":null},{"id":"2301.11911","title":"Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees","created":"2023-01-27","cited_by_count":50,"area":null},{"id":"2303.12872","title":"Human Uncertainty in Concept-Based AI Systems","created":"2023-03-22","cited_by_count":39,"area":null},{"id":"2402.12240","title":"BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts","created":"2024-02-19","cited_by_count":37,"area":null},{"id":"2311.11108","title":"Auxiliary Losses for Learning Generalizable Concept-based Models","created":"2023-11-18","cited_by_count":35,"area":null},{"id":"2401.14142","title":"Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations","created":"2024-01-25","cited_by_count":33,"area":null},{"id":"2405.16507","title":"Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning","created":"2024-05-26","cited_by_count":29,"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":"2309.12482","title":"State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding","created":"2023-09-21","cited_by_count":17,"area":null},{"id":"2406.00519","title":"Learning Discrete Concepts in Latent Hierarchical Models","created":"2024-06-01","cited_by_count":17,"area":null},{"id":"2503.04363","title":"Causally Reliable Concept Bottleneck Models","created":"2025-03-06","cited_by_count":17,"area":null},{"id":"2405.18217","title":"Understanding Inter-Concept Relationships in Concept-Based Models","created":"2024-05-28","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":"2508.10731","title":"Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction","created":"2025-08-14","cited_by_count":13,"area":null},{"id":"2403.05636","title":"Tuning-Free Accountable Intervention for LLM Deployment -- A Metacognitive Approach","created":"2024-03-08","cited_by_count":11,"area":null},{"id":"2307.05350","title":"Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat","created":"2023-07-07","cited_by_count":10,"area":null},{"id":"2303.14496","title":"Learning with Explanation Constraints","created":"2023-03-25","cited_by_count":8,"area":null},{"id":"2305.15775","title":"Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace","created":"2023-05-25","cited_by_count":7,"area":null},{"id":"2306.08424","title":"Selective Concept Models: Permitting Stakeholder Customisation at Test-Time","created":"2023-06-14","cited_by_count":6,"area":null},{"id":"2307.00316","title":"SHARCS: Shared Concept Space for Explainable Multimodal Learning","created":"2023-07-01","cited_by_count":6,"area":null},{"id":"2405.01531","title":"Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models","created":"2024-05-02","cited_by_count":6,"area":null},{"id":"2410.21494","title":"Towards Multi-dimensional Explanation Alignment for Medical Classification","created":"2024-10-28","cited_by_count":6,"area":null},{"id":"2503.18695","title":"OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad","created":"2025-03-24","cited_by_count":6,"area":null},{"id":"2507.23784","title":"SUB: Benchmarking CBM Generalization via Synthetic Attribute Substitutions","created":"2025-07-31","cited_by_count":6,"area":null},{"id":"2308.15096","title":"How Faithful are Self-Explainable GNNs?","created":"2023-08-29","cited_by_count":5,"area":null},{"id":"2405.16508","title":"AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model","created":"2024-05-26","cited_by_count":5,"area":null},{"id":"2406.17931","title":"CAT: Interpretable Concept-based Taylor Additive Models","created":"2024-06-25","cited_by_count":5,"area":null},{"id":"2505.16705","title":"An Analysis of Concept Bottleneck Models: Measuring, Understanding, and Mitigating the Impact of Noisy Annotations","created":"2025-05-22","cited_by_count":5,"area":null},{"id":"2305.17303","title":"Distilling BlackBox to Interpretable models for Efficient Transfer Learning","created":"2023-05-26","cited_by_count":4,"area":null},{"id":"2401.13296","title":"Visual Objectification in Films: Towards a New AI Task for Video Interpretation","created":"2024-01-24","cited_by_count":4,"area":null},{"id":"2403.07078","title":"Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning","created":"2024-03-11","cited_by_count":4,"area":null},{"id":"2502.09018","title":"Zero-shot Concept Bottleneck Models","created":"2025-02-13","cited_by_count":4,"area":null},{"id":"2505.05195","title":"Concept-Based Unsupervised Domain Adaptation","created":"2025-05-08","cited_by_count":4,"area":null},{"id":"2506.12568","title":"MVP-CBM:Multi-layer Visual Preference-enhanced Concept Bottleneck Model for Explainable Medical Image Classification","created":"2025-06-14","cited_by_count":4,"area":null},{"id":"2211.10630","title":"Explainable fetal ultrasound quality assessment with progressive concept bottleneck models","created":"2022-11-19","cited_by_count":3,"area":null},{"id":"2504.03978","title":"V-CEM: Bridging Performance and Intervenability in Concept-based Models","created":"2025-04-04","cited_by_count":3,"area":null},{"id":"2506.05014","title":"Towards Reasonable Concept Bottleneck Models","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2302.10289","title":"Tackling Shortcut Learning in Deep Neural Networks: An Iterative Approach with Interpretable Models","created":"2023-02-20","cited_by_count":2,"area":null},{"id":"2307.13698","title":"Exploring the Lottery Ticket Hypothesis with Explainability Methods: Insights into Sparse Network Performance","created":"2023-07-07","cited_by_count":2,"area":null},{"id":"2407.19300","title":"CoLiDR: Concept Learning using Aggregated Disentangled Representations","created":"2024-07-27","cited_by_count":2,"area":null},{"id":"2502.20130","title":"QPM: Discrete Optimization for Globally Interpretable Image Classification","created":"2025-02-27","cited_by_count":2,"area":null},{"id":"2602.02886","title":"Mixture of Concept Bottleneck Experts","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2602.14626","title":"Concepts' Information Bottleneck Models","created":"2026-02-16","cited_by_count":2,"area":null},{"id":"2303.06635","title":"Schema Inference for Interpretable Image Classification","created":"2023-03-12","cited_by_count":1,"area":null},{"id":"2505.22084","title":"MObyGaze: a film dataset of multimodal objectification densely annotated by experts","created":"2025-05-28","cited_by_count":1,"area":null},{"id":"2302.01976","title":"SPARLING: Learning Latent Representations with Extremely Sparse Activations","created":"2023-02-03","cited_by_count":0,"area":null},{"id":"2403.14140","title":"Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks","created":"2024-03-21","cited_by_count":0,"area":null},{"id":"2411.09639","title":"MCCE: Missingness-aware Causal Concept Explainer","created":"2024-11-14","cited_by_count":0,"area":null},{"id":"2502.01191","title":"Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model","created":"2025-02-03","cited_by_count":0,"area":null},{"id":"2502.06775","title":"Enhancing Performance of Explainable AI Models with Constrained Concept Refinement","created":"2025-02-10","cited_by_count":0,"area":null},{"id":"2510.10063","title":"CLMN: Concept based Language Models via Neural Symbolic Reasoning","created":"2025-10-11","cited_by_count":0,"area":"lr"},{"id":"2603.13163","title":"Towards Faithful Multimodal Concept Bottleneck Models","created":"2026-03-13","cited_by_count":0,"area":null},{"id":"2606.04326","title":"Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.10669","title":"In Defense of Information Leakage in Concept-based Models","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2608.22584","title":"Weakly supervised concept Bottleneck Learning for Robust Two stage Object centric visual reasoning","created":"2026-08-23","cited_by_count":0,"area":"lr"}],"next_cursor":null}