Search Results for author:
Found 5 papers, 4 papers with code
Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI
This paper quantifies the quality of heatmap-based eXplainable AI (XAI) methods w. r. t image classification problem.
Self Reward Design with Fine-grained Interpretability
This paper proposes a way to circumvent the issues through the bottom-up design of neural networks with detailed interpretability, where each neuron or layer has its own meaning and utility that corresponds to humanly understandable concept.
Two Instances of Interpretable Neural Network for Universal Approximations
This paper proposes two bottom-up interpretable neural network (NN) constructions for universal approximation, namely Triangularly-constructed NN (TNN) and Semi-Quantized Activation NN (SQANN).
Convolutional Neural Network Interpretability with General Pattern Theory
Ongoing efforts to understand deep neural networks (DNN) have provided many insights, but DNNs remain incompletely understood.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network
The practical application of deep neural networks are still limited by their lack of transparency.
