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stat.ML2020★ 1 cited
Bayesian neural networks and dimensionality reduction
Deborshee Sen, Theodore Papamarkou, David Dunson
In conducting non-linear dimensionality reduction and feature learning, it is common to suppose that the data lie near a lower-dimensional manifold. A class of model-based approach…
stat.ML2020
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes
Devanshu Agrawal, Theodore Papamarkou, Jacob Hinkle
There has recently been much work on the "wide limit" of neural networks, where Bayesian neural networks (BNNs) are shown to converge to a Gaussian process (GP) as all hidden layer…