Attentive Class Activation Token (AttCAT)
A novel Transformer explanation technique via attentive class activation tokens, aka, AttCAT, leveraging encoded features, their gradients, and their attention weights to generate a faithful and confident explanation for Transformer’s output
Qiang, Y, Pan, D, Li, C, Li, X, Jang, R, and Zhu, D. (2022) AttCAT: Explaining Transformers via Attentive Class Activation Tokens. In the Proceedings of Thirty-sixth Conference on Neural Information Processing Systems (NuerIPS-22), New Orleans, LA, USA.
Adversarial Gradient Integration (AGI) to explain the attribution of each pixel or each token to the DNN’s class prediction via integrating gradients from adversarial examples to the test examples for the target class.
Pan, D, Li, X and Zhu, D. (2021) Explaining Deep Neural Network Models with Adversarial Gradient Integration. In 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Montreal, Canada.
An interpretable neural network architecture allowing interpretable feature mapping via dissecting latent layers guided by aspects and demonstrated applications in explainable recommender’s system.
Pan, D, Li, X, Li, X and Zhu, D. (2020) Explainable recommendation via interpretable feature mapping and evaluating explainability. In the proceedings of 29th International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan.
A new Probabilistically Compact loss (PC loss) to directly enlarge the probability gap between true class and false classes to improve adversarial robustness of DNNs.
Li, X, Li, X, Pan,D and Zhu, D. (2021) Improving adversarial robustness via probabilistically compact loss with logit constraints. In the proceedings of Thirty-Five AAAI Conference on Artificial Intelligence (AAAI-21), virtual conference.
Attribution map generated by CIA
A novel generative data augmentation approach to create counterfactual samples to make the sensitive attribute and the target attribute d-separated to achieve fairness and the interpolation path ensures attribution based explainability.
Qiang, Y, Li, C, Brocanelli, M, Zhu, D. (2022) Counterfactual Interpolation Augmentation (CIA): A Unified Approach to Enhance Fairness and Explainability of DNN. In the proceedings of 31st International Joint Conference on Artificial Intelligence (IJCAI-22), Messe Wien, Vienna, Austria.
A novel reweighted logistic loss for multi-class classification to improve ordinary logistic loss by focusing on learning hard non-target classes (target vs. non-target class in one-vs.-all)
Li, X, Li, X, Pan,D and Zhu, D. (2020) On the learning behavior of logistic and softmax losses for deep neural networks. In the proceedings of Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, USA.
Existing DNN predictive models either relies on pre-defined patient subgroups or one-size-fit-all. We develop a novel Deep Mixture Neural Network based predictive models for patient stratification and group-specific risk factor prioritization.
Li, X, Zhu, D* and Levy, P (2020) Predicting clinical outcomes with patient stratification via deep mixture neural networks. American Medical Informatics Association (AMIA-20) Summit on Clinical Research Informatics, Houston, USA. (Best Student Paper Award, *Corresponding Autor) PubMed 32477657
Many behavior disorders, e.g., obesity, are multi-faced health outcome and risk factors are highly specific to certain subpopulation groups residing in different geospatial districts. We develop a Multi-Task Learning (MTL) approach for prioritize multi-level risk factors.
Wang, Dong, M, Towner, E and Zhu, D. (2019) Prioritization of multi-level risk factors for obesity. In the proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM-19), 1065-1072.
We design a deep 3D CNN to fetal blood oxygen-level dependence (BOLD) resting-state fMRI data to isolate the variation in fMRI signals that relates to the age effect.
Li, X., Hect, J., Thompson, J. and Zhu, D. (2020). Interpreting age effects of human fetal brain from spontaneous fMRI using deep 3D convolutional neural networks. IEEE International Symposium on Biomedical Imaging (ISBI-20), Iowa City, USA.
We present Vispi, an automatic medical image interpretation system, which first annotates an image via classifying and localizing common thoracic diseases with visual support and then followed by report generation from an attentive LSTM model.
Li, X., Cao, R., & Zhu, D. (2020). Vispi: Automatic visual perception and interpretation of chest X-rays. In the proceedings of the Medical Imaging with Deep Learning (MIDL-20) conference, Montreal, CA.
Deng N, Puetter, A, Zhang, K, Johnson, K., Zhao, Z, Taylor, C, Flemington, E and Zhu, D. (2011) Isoform-level microRNA-155 Target Prediction using RNA-seq. Nucleic Acids Res., doi: 10.1093/nar/gkr042.
Judeh, T, Johnson, C, Kumar, A, Zhu, D (2013) TEAK: Topological Enrichment Analysis frameworK for detecting activated biological subpathways. Nucleic Acids Res., doi: 10.1093/nar/gks1299.
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