no code implementations • 3 Nov 2020 • Alexander Wong, Andrew Hryniowski, Xiao Yu Wang
In this study we explore the feasibility and utility of a multi-scale trust quantification strategy to gain insights into the fairness of a financial deep learning model, particularly under different scenarios at different scales.
no code implementations • 30 Sep 2020 • Andrew Hryniowski, Xiao Yu Wang, Alexander Wong
We experimentally leverage trust matrices to study several well-known deep neural network architectures for image recognition, and further study the trust density and conditional trust densities for an interesting actor-oracle answer scenario.
no code implementations • 12 Sep 2020 • Alexander Wong, Xiao Yu Wang, Andrew Hryniowski
In this study, we take a step towards simple, interpretable metrics for trust quantification by introducing a suite of metrics for assessing the overall trustworthiness of deep neural networks based on their behaviour when answering a set of questions.
no code implementations • 16 Oct 2019 • Zhong Qiu Lin, Mohammad Javad Shafiee, Stanislav Bochkarev, Michael St. Jules, Xiao Yu Wang, Alexander Wong
A comprehensive analysis using this approach was conducted on several state-of-the-art explainability methods (LIME, SHAP, Expected Gradients, GSInquire) on a ResNet-50 deep convolutional neural network using a subset of ImageNet for the task of image classification.
no code implementations • 20 Dec 2014 • Alexander Wong, Mohammad Javad Shafiee, Parthipan Siva, Xiao Yu Wang
In this study, we investigate the feasibility of unifying fully-connected and deep-structured models in a computationally tractable manner for the purpose of structured inference.