Article

VPNet: A Unified Explainable Classification Framework Using Probabilistic Circuits and Foundation Models

2025-10-03

Abstract excerpt

Deep learning-based classification models can learn and extract complex representations or relationships between classes. If the decision process of such AI models can be visualized in a way that humans can understand, this may create the potential for AI tools to assist in new scientific discoveries and improve trust in AI models. However, visualizations produced by current post-hoc XAI methods, including Grad-CA...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 0ec292ef-1f72-59bf-9682-48ff38148453
DOI: 10.1101/2025.09.30.25336962

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

VPNet: A Unified Explainable Classification Framework Using Probabilistic Circuits and Foundation ModelsDOI 10.1101/2025.09.30.25336962Read

GradLIME: A CNN Local Interpretation Model Based on Feature Gradient ActivationSemantic proximity Read

Abstraction, Validation, and Generalization for Explainable Artificial IntelligenceSemantic proximity Read

Quantitative Attributions with CounterfactualsSemantic proximity Read

Neural networks for abstraction and reasoning: Towards broad generalization in machinesSemantic proximity Read

Learning Representations by Humans, for HumansSemantic proximity Read

Specxai - Spectral Interpretability of Deep Learning ModelsSemantic proximity Read

Towards Trustworthy Breast Cancer Diagnosis: A Comparative Explainability Stability Study of DenseNet121 and Vision TransformersSemantic proximity Read

Explainable artificial intelligence for neuroimaging-based dementia diagnosis and prognosisSemantic proximity Read

CogNGen: Constructing the kernel of a hyperdimensional predictive processing cognitive architectureSemantic proximity Read

Parameter-efficient Prompt Tuning of VisionFoundation Model With Adaptive Focal Loss forInterpretable MCI ScreeningSemantic proximity Read

Scaffolded representation learning in deep networksSemantic proximity Read

Chimeric U-Net – Modifying the standard U-Net towards ExplainabilitySemantic proximity Read