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Choi, \"Whole slide image analysis and detection of prostate cancer using vision transformers\", in 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2022: IEEE, pp. 399-402."},{"key":"e_1_3_2_1_23_1","volume-title":"Neurocomputing","author":"Shen L.","year":"2022","unstructured":"L. Shen and Y. Wang , \" TCCT: tightly-coupled convolutional transformer on time series forecasting \", Neurocomputing , 2022 . L. Shen and Y. Wang, \"TCCT: tightly-coupled convolutional transformer on time series forecasting\", Neurocomputing, 2022."},{"key":"e_1_3_2_1_24_1","volume-title":"2021 IEEE International Conference on Big Data (Big Data)","author":"Narayan A.","unstructured":"A. Narayan , B. S. Mishra , P. S. Hiremath , N. T. Pendari , and S. Gangisetty , \" An Ensemble of transformer and LSTM approach for multivariate time series data classification \", in 2021 IEEE International Conference on Big Data (Big Data) , 2021: IEEE, pp. 5774-5779. A. 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Liu , \"Variable selection with rigorous uncertainty quantification using deep Bayesian neural networks: Posterior concentration and Bernstein-von Mises phenomenon \", in International Conference on Artificial Intelligence and Statistics , 2021: PMLR, pp. 3124-3132. J. Liu, \"Variable selection with rigorous uncertainty quantification using deep Bayesian neural networks: Posterior concentration and Bernstein-von Mises phenomenon\", in International Conference on Artificial Intelligence and Statistics, 2021: PMLR, pp. 3124-3132."},{"key":"e_1_3_2_1_27_1","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Wang Y.","unstructured":"Y. Wang and V. Rockov\u00e1 , \" Uncertainty quantification for sparse deep learning \", in International Conference on Artificial Intelligence and Statistics , 2020: PMLR, pp. 298-308. Y. Wang and V. Rockov\u00e1, \"Uncertainty quantification for sparse deep learning\", in International Conference on Artificial Intelligence and Statistics, 2020: PMLR, pp. 298-308."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2019.106816"},{"issue":"1","key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","first-page":"20","DOI":"10.3847\/1538-4365\/ac14b7","article-title":"Tracing H\u03b1 fibrils through Bayesian deep learning","volume":"256","author":"Jiang H.","year":"2021","unstructured":"H. Jiang , J. Jing , J. Wang , C. Liu , Q. Li , Y. Xu , J. T. L. Wang , and H. Wang , \" Tracing H\u03b1 fibrils through Bayesian deep learning \", The Astrophysical Journal Supplement Series , vol. 256 , no. 1 , p. 20 , 2021 . H. Jiang, J. Jing, J. Wang, C. Liu, Q. Li, Y. Xu, J. T. L. Wang, and H. 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