339 citations · 686 across the 11 of their papers we have counts for
13 papers
False Sense of Security in Explainable Artificial Intelligence (XAI)
Neo Christopher Chung, Hongkyou Chung, Hearim Lee +3
A cautious interpretation of AI regulations and policy in the EU and the USA place explainability as a central deliverable of compliant AI systems. However, from a technical perspe…
Class-Discriminative Attention Maps for Vision Transformers
Lennart Brocki, Jakub Binda, Neo Christopher Chung
Importance estimators are explainability methods that quantify feature importance for deep neural networks (DNN). In vision transformers (ViT), the self-attention mechanism natural…
Challenges of Large Language Models for Mental Health Counseling
Neo Christopher Chung, George Dyer, Lennart Brocki
The global mental health crisis is looming with a rapid increase in mental disorders, limited resources, and the social stigma of seeking treatment. As the field of artificial inte…
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models
Lennart Brocki, Neo Christopher Chung
Despite the unprecedented performance of deep neural networks (DNNs) in computer vision, their practical application in the diagnosis and prognosis of cancer using medical imaging…
Feature Perturbation Augmentation for Reliable Evaluation of Importance Estimators in Neural Networks
Lennart Brocki, Neo Christopher Chung
Post-hoc explanation methods attempt to make the inner workings of deep neural networks more interpretable. However, since a ground truth is in general lacking, local post-hoc inte…
Deep Learning Mental Health Dialogue System
Lennart Brocki, George C. Dyer, Anna Gładka +1
Mental health counseling remains a major challenge in modern society due to cost, stigma, fear, and unavailability. We posit that generative artificial intelligence (AI) models des…
Evaluation of importance estimators in deep learning classifiers for Computed Tomography
Lennart Brocki, Wistan Marchadour, Jonas Maison +5
Deep learning has shown superb performance in detecting objects and classifying images, ensuring a great promise for analyzing medical imaging. Translating the success of deep lear…
Fidelity of Interpretability Methods and Perturbation Artifacts in Neural Networks
Lennart Brocki, Neo Christopher Chung
Despite excellent performance of deep neural networks (DNNs) in image classification, detection, and prediction, characterizing how DNNs make a given decision remains an open probl…
Human in the Loop for Machine Creativity
Neo Christopher Chung
Artificial intelligence (AI) is increasingly utilized in synthesizing visuals, texts, and audio. These AI-based works, often derived from neural networks, are entering the mainstre…
Input Bias in Rectified Gradients and Modified Saliency Maps
Lennart Brocki, Neo Christopher Chung
Interpretation and improvement of deep neural networks relies on better understanding of their underlying mechanisms. In particular, gradients of classes or concepts with respect t…
Concept Saliency Maps to Visualize Relevant Features in Deep Generative Models
Lennart Brocki, Neo Christopher Chung
Evaluating, explaining, and visualizing high-level concepts in generative models, such as variational autoencoders (VAEs), is challenging in part due to a lack of known prediction…
Jaccard/Tanimoto similarity test and estimation methods
Neo Christopher Chung, Błażej Miasojedow, Michał Startek +1
Binary data are used in a broad area of biological sciences. Using binary presence-absence data, we can evaluate species co-occurrences that help elucidate relationships among orga…
Statistical significance of variables driving systematic variation
Neo Christopher Chung, John D. Storey
There are a number of well-established methods such as principal components analysis (PCA) for automatically capturing systematic variation due to latent variables in large-scale g…