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Found 8 papers, 6 papers with code
Explaining Deep Neural Networks by Leveraging Intrinsic Methods
This thesis addresses this issue by contributing to the field of eXplainable AI, focusing on enhancing the interpretability of deep neural networks.
Towards a fuller understanding of neurons with Clustered Compositional Explanations
Compositional Explanations is a method for identifying logical formulas of concepts that approximate the neurons' behavior.
State of the Art of Visual Analytics for eXplainable Deep Learning
The survey concludes by identifying future research challenges and bridging activities that are helpful to strengthen the role of Visual Analytics as effective support for eXplainable Deep Learning and to foster the adoption of Visual Analytics solutions in the eXplainable Deep Learning community.
Prototype-based Interpretable Graph Neural Networks
We then evaluate the explanations of the interpretable models by comparing them with post-hoc approaches and self-explainable models.
A self-interpretable module for deep image classification on small data
This paper presents Memory Wrap, a module (i. e, a set of layers) that can be added to deep learning models to improve their performance and interpretability in settings where few data are available.
Detection Accuracy for Evaluating Compositional Explanations of Units
The recent success of deep learning models in solving complex problems and in different domains has increased interest in understanding what they learn.
Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Models
Due to their black-box and data-hungry nature, deep learning techniques are not yet widely adopted for real-world applications in critical domains, like healthcare and justice.
Explainable Inference on Sequential Data via Memory-Tracking
Our results show that we are able to explain agent’s decisions in (1) and to reconstruct the most relevant sentences used by the network to select the story ending in (2).
