Binary and multiclass comparison of Bayesian Neural Network and Simple Neural Network
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Updated
Jan 18, 2020 - Jupyter Notebook
Binary and multiclass comparison of Bayesian Neural Network and Simple Neural Network
(Forked Version) Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"
Bayesian deep learning experiments
An experimental Python package for learning Bayesian Neural Network.
Deep Modeling of Strong Gravitational Time Delay Lenses for Bayesian Inference of the Hubble Constant
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This is a PyTorch implementation of a Bayesian Convolutional Neural Network (BCNN) for Semantic Scene Completion on the SUNCG dataset. Given a depth image the network outputs a semantic segmentation and entropy score in 3D voxel format.
Model which can predict COVID-19 positive case from axial lung CT-scan images.
Deep Generative Bayesian Network
[NeurIPS'23] Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
[NeurIPS 2023] Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
Bayesian neural networks in PyTorch
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