
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning
Gaussian processes remain popular as a flexible and expressive model cla...
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The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Large width limits have been a recent focus of deep learning research: m...
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Rectangular Flows for Manifold Learning
Normalizing flows are invertible neural networks with tractable changeo...
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Simulating time to event prediction with spatiotemporal echocardiography deep learning
Integrating methods for timetoevent prediction with diagnostic imaging...
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Medical Imaging and Machine Learning
Advances in computing power, deep learning architectures, and expert lab...
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Predicting postoperative right ventricular failure using videobased deep learning
Noninvasive and cost effective in nature, the echocardiogram allows for...
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BiasFree Scalable Gaussian Processes via Randomized Truncations
Scalable Gaussian Process methods are computationally attractive, yet in...
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Uses and Abuses of the CrossEntropy Loss: Case Studies in Modern Deep Learning
Modern deep learning is primarily an experimental science, in which empi...
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General lineartime inference for Gaussian Processes on one dimension
Gaussian Processes (GPs) provide a powerful probabilistic framework for ...
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The continuous categorical: a novel simplexvalued exponential family
Simplexvalued data appear throughout statistics and machine learning, f...
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Paraphrase Generation with Latent Bag of Words
Paraphrase generation is a longstanding important problem in natural lan...
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Invertible Gaussian Reparameterization: Revisiting the GumbelSoftmax
The GumbelSoftmax is a continuous distribution over the simplex that is...
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The continuous Bernoulli: fixing a pervasive error in variational autoencoders
Variational autoencoders (VAE) have quickly become a central tool in mac...
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Approximating exponential family models (not single distributions) with a twonetwork architecture
Recently much attention has been paid to deep generative models, since t...
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Deep Random Splines for Point Process Intensity Estimation
Gaussian processes are the leading class of distributions on random func...
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A Probabilistic Model of Cardiac Physiology and Electrocardiograms
An electrocardiogram (EKG) is a common, noninvasive test that measures ...
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Calibrating Deep Convolutional Gaussian Processes
The wide adoption of Convolutional Neural Networks (CNNs) in application...
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Bayesian estimation for large scale multivariate OrnsteinUhlenbeck model of brain connectivity
Estimation of reliable wholebrain connectivity is a crucial step toward...
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Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Many matching, tracking, sorting, and ranking problems require probabili...
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Maximum Entropy Flow Networks
Maximum entropy modeling is a flexible and popular framework for formula...
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Linear dynamical neural population models through nonlinear embeddings
A body of recent work in modeling neural activity focuses on recovering ...
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Bayesian Learning of Kernel Embeddings
Kernel methods are one of the mainstays of machine learning, but the pro...
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Preconditioning Kernel Matrices
The computational and storage complexity of kernel machines presents the...
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Neuroprosthetic decoder training as imitation learning
Neuroprosthetic braincomputer interfaces function via an algorithm whic...
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Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
A common approach for Bayesian computation with big data is to partition...
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Scaling Multidimensional Inference for Structured Gaussian Processes
Exact Gaussian Process (GP) regression has O(N^3) runtime for data size ...
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Gaussian Probabilities and Expectation Propagation
While Gaussian probability densities are omnipresent in applied mathemat...
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John P. Cunningham
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