U-FaceBP combines multiple Bayesian neural networks in an ensemble to estimate blood pressure from face video modalities while quantifying uncertainty, showing improved performance on datasets with 1197 diverse subjects.
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Convolutional neural networks (CNNs) work well on large datasets. But labelled data is hard to collect, and in some applications larger amounts of data are not available. The problem then is how to use CNNs with small data -- as CNNs overfit quickly. We present an efficient Bayesian CNN, offering better robustness to over-fitting on small data than traditional approaches. This is by placing a probability distribution over the CNN's kernels. We approximate our model's intractable posterior with Bernoulli variational distributions, requiring no additional model parameters. On the theoretical side, we cast dropout network training as approximate inference in Bayesian neural networks. This allows us to implement our model using existing tools in deep learning with no increase in time complexity, while highlighting a negative result in the field. We show a considerable improvement in classification accuracy compared to standard techniques and improve on published state-of-the-art results for CIFAR-10.
verdicts
UNVERDICTED 5representative citing papers
A new regularization approach for unsupervised domain adaptation that calibrates Renyi entropy of uncertainties estimated via variational Bayes.
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.
A hybrid approach applies active learning at edge devices and federated learning at fog nodes to reduce training data volume and communication cost for image classification in distributed edge-fog setups.
A survey of deep learning approaches for regressing absolute camera pose from single RGB images, covering key methods, trends, cross-comparisons, and reproducibility notes.
citing papers explorer
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U-FaceBP: Uncertainty-aware Bayesian Ensemble Deep Learning for Face Video-based Blood Pressure Estimation
U-FaceBP combines multiple Bayesian neural networks in an ensemble to estimate blood pressure from face video modalities while quantifying uncertainty, showing improved performance on datasets with 1197 diverse subjects.
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Unsupervised Domain Adaptation via Calibrating Uncertainties
A new regularization approach for unsupervised domain adaptation that calibrates Renyi entropy of uncertainties estimated via variational Bayes.
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Are Candidate Models Really Needed for Active Learning?
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.
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Active Learning Solution on Distributed Edge Computing
A hybrid approach applies active learning at edge devices and federated learning at fog nodes to reduce training data volume and communication cost for image classification in distributed edge-fog setups.
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Introduction to Camera Pose Estimation with Deep Learning
A survey of deep learning approaches for regressing absolute camera pose from single RGB images, covering key methods, trends, cross-comparisons, and reproducibility notes.