{"total":35,"rows":[{"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":"2410.20722","title":"Interpretable Image Classification with Adaptive Prototype-based Vision Transformers","created":"2024-10-28","cited_by_count":25,"area":null},{"id":"2410.02331","title":"Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks","created":"2024-10-03","cited_by_count":18,"area":null},{"id":"2501.15638","title":"A Comprehensive Survey on Self-Interpretable Neural Networks","created":"2025-01-26","cited_by_count":18,"area":null},{"id":"2405.14331","title":"LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision","created":"2024-05-23","cited_by_count":15,"area":null},{"id":"2503.01087","title":"Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time","created":"2025-03-03","cited_by_count":13,"area":null},{"id":"2501.09333","title":"Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis","created":"2025-01-16","cited_by_count":12,"area":null},{"id":"2312.00092","title":"Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition","created":"2023-11-30","cited_by_count":11,"area":null},{"id":"2406.14675","title":"This Looks Better than That: Better Interpretable Models with ProtoPNeXt","created":"2024-06-20","cited_by_count":11,"area":null},{"id":"2404.10420","title":"AudioProtoPNet: An interpretable deep learning model for bird sound classification","created":"2024-04-16","cited_by_count":10,"area":null},{"id":"2406.10025","title":"ProtoS-ViT: Visual foundation models for sparse self-explainable classifications","created":"2024-06-14","cited_by_count":8,"area":null},{"id":"2406.03421","title":"Post-hoc Part-prototype Networks","created":"2024-06-05","cited_by_count":7,"area":null},{"id":"2509.17050","title":"Geodesic Prototype Matching via Diffusion Maps for Interpretable Fine-Grained Recognition","created":"2025-09-21","cited_by_count":6,"area":null},{"id":"2410.08925","title":"An Overview of Prototype Formulations for Interpretable Deep Learning","created":"2024-10-11","cited_by_count":5,"area":null},{"id":"2504.08713","title":"ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning","created":"2025-04-11","cited_by_count":4,"area":null},{"id":"2508.14562","title":"Locality-aware Concept Bottleneck Model","created":"2025-08-20","cited_by_count":4,"area":null},{"id":"2407.12200","title":"This Probably Looks Exactly Like That: An Invertible Prototypical Network","created":"2024-07-16","cited_by_count":3,"area":null},{"id":"2411.01644","title":"Achieving Domain-Independent Certified Robustness via Knowledge Continuity","created":"2024-11-03","cited_by_count":3,"area":null},{"id":"2502.09340","title":"This looks like what? Challenges and Future Research Directions for Part-Prototype Models","created":"2025-02-13","cited_by_count":3,"area":null},{"id":"2506.05533","title":"Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2503.06084","title":"Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts","created":"2025-03-08","cited_by_count":2,"area":null},{"id":"2507.11099","title":"A Survey on Interpretability in Visual Recognition","created":"2025-07-15","cited_by_count":2,"area":null},{"id":"2604.16076","title":"Prototype-Grounded Concept Models for Verifiable Concept Alignment","created":"2026-04-17","cited_by_count":2,"area":null},{"id":"2406.06386","title":"FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography","created":"2024-06-10","cited_by_count":1,"area":null},{"id":"2410.18705","title":"Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks","created":"2024-10-24","cited_by_count":1,"area":null},{"id":"2501.06059","title":"COMIX: Compositional Explanations using Prototypes","created":"2025-01-10","cited_by_count":1,"area":null},{"id":"2507.06819","title":"Comprehensive Evaluation of Prototype Neural Networks","created":"2025-07-09","cited_by_count":1,"area":null},{"id":"2409.02335","title":"What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits","created":"2024-09-03","cited_by_count":0,"area":null},{"id":"2411.18645","title":"Bi-ICE: An Inner Interpretable Framework for Image Classification via Bi-directional Interactions between Concept and Input Embeddings","created":"2024-11-26","cited_by_count":0,"area":null},{"id":"2503.12978","title":"Enhancing Job Salary Prediction with Disentangled Composition Effect Modeling: A Neural Prototyping Approach","created":"2025-03-17","cited_by_count":0,"area":null},{"id":"2511.19752","title":"What You See is (Usually) What You Get: Multimodal Prototype Networks that Abstain from Expensive Modalities","created":"2025-11-24","cited_by_count":0,"area":null},{"id":"2601.12303","title":"Concepts from Representations: Post-hoc Concept Bottleneck Models via Sparse Decomposition of Visual Representations","created":"2026-01-18","cited_by_count":0,"area":null},{"id":"2606.27582","title":"Beyond Points: Spherical Distributional Part Prototypes for Interpretable Classification","created":"2026-06-25","cited_by_count":0,"area":null},{"id":"2606.30901","title":"GRAPE: Graph-Augmented Prototype Explanations for Interactive Medical Image Diagnosis","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2609.27194","title":"Diverse by Design: Architectural Constraints for Prototype-Based Interpretability","created":"2026-09-23","cited_by_count":0,"area":null}],"next_cursor":null}