When Gaussian process meets big data: A review of scalable GPs
The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …
hardware encourages success stories in the machine learning community. In the …
Priors in bayesian deep learning: A review
V Fortuin - International Statistical Review, 2022 - Wiley Online Library
While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …
recent Bayesian deep learning models have often fallen back on vague priors, such as …
Scaling limits of wide neural networks with weight sharing: Gaussian process behavior, gradient independence, and neural tangent kernel derivation
G Yang - arxiv preprint arxiv:1902.04760, 2019 - arxiv.org
Several recent trends in machine learning theory and practice, from the design of state-of-
the-art Gaussian Process to the convergence analysis of deep neural nets (DNNs) under …
the-art Gaussian Process to the convergence analysis of deep neural nets (DNNs) under …
Deep convolutional networks as shallow gaussian processes
We show that the output of a (residual) convolutional neural network (CNN) with an
appropriate prior over the weights and biases is a Gaussian process (GP) in the limit of …
appropriate prior over the weights and biases is a Gaussian process (GP) in the limit of …
Wide feedforward or recurrent neural networks of any architecture are gaussian processes
G Yang - Advances in Neural Information Processing …, 2019 - proceedings.neurips.cc
Wide neural networks with random weights and biases are Gaussian processes, as
observed by Neal (1995) for shallow networks, and more recently by Lee et al.~(2018) and …
observed by Neal (1995) for shallow networks, and more recently by Lee et al.~(2018) and …
Deep convolutional Gaussian processes
We propose deep convolutional Gaussian processes, a deep Gaussian process architecture
with convolutional structure. The model is a principled Bayesian framework for detecting …
with convolutional structure. The model is a principled Bayesian framework for detecting …
Image matting with deep gaussian process
Y Zheng, Y Yang, T Che, S Hou… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
We observe a common characteristic between the classical propagation-based image
matting and the Gaussian process (GP)-based regression. The former produces closer …
matting and the Gaussian process (GP)-based regression. The former produces closer …
Probabilistic numeric convolutional neural networks
Continuous input signals like images and time series that are irregularly sampled or have
missing values are challenging for existing deep learning methods. Coherently defined …
missing values are challenging for existing deep learning methods. Coherently defined …
Classification of cassava leaf diseases using deep Gaussian transfer learning model
Abstract In Sub‐Saharan Africa, experts visually examine the plants and look for disease
symptoms on the leaves to diagnose cassava diseases, a subjective method. Machine …
symptoms on the leaves to diagnose cassava diseases, a subjective method. Machine …
Ischemic stroke lesion prediction using imbalanced temporal deep gaussian process (iTDGP)
As one of the leading causes of mortality and disability worldwide, Acute Ischemic Stroke
(AIS) occurs when the blood supply to the brain is suddenly interrupted because of a …
(AIS) occurs when the blood supply to the brain is suddenly interrupted because of a …