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Bayesian Deep Learning is a field that combines Bayesian probability theory with deep learning. It aims to provide a principled way of quantifying uncertainty in neural network predictions.
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 Representation-Level Counterfactual Calibration for Debiased Zero-Shot Recognition Pei Peng MingKun Xie Hang Hao Tong Jin ShengJun Huang | |||
 CDFlow: Building Invertible Layers with Circulant and Diagonal Matrices Xuchen Feng Siyu Liao | |||
 Neural Variational Dropout ProcessesInternational Conference on Learning Representations (ICLR), 2025 | |||
 SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity PredictionIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023 | |||
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