arXiv AI
Jun 30
On the Faithfulness of Post-Hoc Concept Bottleneck Models
arXiv:2606. 30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color.
Concept Labels Are Not Enough: Rethinking Concept Bottleneck Models through Representation Integrity
arXiv:2510. 15770v4 Announce Type: replace-cross Abstract: Although deep neural networks achieve strong predictive performance, their internal reasoning often remains difficult to inspect and control.
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arXiv Machine Learning
Jun 16
Assessing Reliability of Symbol Detection in Concept Bottleneck Models
arXiv:2606. 16535v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) are a relevant tool for explainable Artificial Intelligence because they make their predictions through human-interpretable symbols.
arXiv Machine Learning
Aug 12
Clarity: The Flexibility-Interpretability Trade-Off in Sparsity-aware Concept Bottleneck Models
arXiv:2601. 21944v3 Announce Type: replace Abstract: The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability.
arXiv AI
1d ago
Identifying Confusion Trends in Concept-based XAI for Multi-Label Classification
arXiv:2608. 15731v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) deployed in high-risk domains, such as healthcare and autonomous driving, must be not only accurate but also understandable to ensure user trust.
arXiv AI
Jul 7
Explainable Novel Category Discovery in Semantic Concept Space
arXiv:2607. 04548v1 Announce Type: cross Abstract: Novel category discovery aims to identify unseen classes from unlabeled data by transferring knowledge from labeled categories, but most existing methods perform discovery in opaque latent feature spaces.
arXiv Machine Learning
Jun 5
Concept-SAE: A Controllable and Invertible Concept Interface for Sparse Autoencoders
arXiv:2509. 22015v2 Announce Type: replace Abstract: Standard Sparse Autoencoders (SAEs) excel at discovering a dictionary of a model's learned features, providing a powerful lens for passive feature discovery.
arXiv AI
Jul 13
ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI
arXiv:2607. 09649v1 Announce Type: new Abstract: Concept-based explainable artificial intelligence (AI) can make model reasoning more human-understandable, but concept-level outputs are not automatically trustworthy.
arXiv AI
Jun 2
Hoeffding Concept Bottleneck Models with Applications to Overhead Images
arXiv:2606. 00082v1 Announce Type: cross Abstract: Explainability of deep learning algorithms is critical for computer-vision applications with high-stake decisions.
arXiv AI
Jun 15
Adversarial Concept Search: Predicting Compositional Errors From Feature Geometry
arXiv:2606. 13934v1 Announce Type: new Abstract: Humans cannot always intuit what scenarios are most challenging to LLMs.
arXiv AI
Jun 19
Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks
arXiv:2606. 19489v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) enhance interpretability by projecting learned features into a human-understandable concept space.
arXiv Machine Learning
Jun 19
Multimodal Concept Bottleneck Models
arXiv:2606. 19882v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of deep learning networks by aligning the features extracted from images with natural concepts.
arXiv Machine Learning
Jul 24
Concept Concentration for Faithful Representation Intervention
arXiv:2505. 18672v2 Announce Type: replace Abstract: Representation intervention aims to localize and modify the representations that encode the underlying concepts in large language models (LLMs) to elicit the aligned and expected behaviors.