2.4k citations · 2.5k across the 9 of their papers we have counts for
9 papers
Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI
Erico Tjoa, Hong Jing Khok, Tushar Chouhan +1
This paper quantifies the quality of heatmap-based eXplainable AI (XAI) methods w.r.t image classification problem. Here, a heatmap is considered desirable if it improves the proba…
Self Reward Design with Fine-grained Interpretability
Erico Tjoa, Guan Cuntai
The black-box nature of deep neural networks (DNN) has brought to attention the issues of transparency and fairness. Deep Reinforcement Learning (Deep RL or DRL), which uses DNN to…
Two Instances of Interpretable Neural Network for Universal Approximations
Erico Tjoa, Guan Cuntai
This paper proposes two bottom-up interpretable neural network (NN) constructions for universal approximation, namely Triangularly-constructed NN (TNN) and Semi-Quantized Activatio…
A Modified Convolutional Network for Auto-encoding based on Pattern Theory Growth Function
Erico Tjoa
This brief paper reports the shortcoming of a variant of convolutional neural network whose components are developed based on the pattern theory framework.
Convolutional Neural Network Interpretability with General Pattern Theory
Erico Tjoa, Guan Cuntai
Ongoing efforts to understand deep neural networks (DNN) have provided many insights, but DNNs remain incompletely understood. Improving DNN's interpretability has practical benefi…
Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network
Erico Tjoa, Guan Cuntai
The practical application of deep neural networks are still limited by their lack of transparency. One of the efforts to provide explanation for decisions made by artificial intell…
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset
Erico Tjoa, Cuntai Guan
Post-hoc analysis is a popular category in eXplainable artificial intelligence (XAI) study. In particular, methods that generate heatmaps have been used to explain the deep neural…
Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks
Erico Tjoa, Guo Heng, Lu Yuhao +1
We implement a visual interpretability method Layer-wise Relevance Propagation (LRP) on top of 3D U-Net trained to perform lesion segmentation on the small dataset of multi-modal i…
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI
Erico Tjoa, Cuntai Guan
Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, esp…