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06/30/2023
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Diffusion models have been successful on a range of conditional generati...
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02/01/2023
Pathologies of Predictive Diversity in Deep Ensembles
Classical results establish that ensembles of small models benefit when ...
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11/30/2022
Denoising Deep Generative Models
Likelihood-based deep generative models have recently been shown to exhi...
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05/30/2022
Posterior and Computational Uncertainty in Gaussian Processes
Gaussian processes scale prohibitively with the size of the dataset. In ...
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05/20/2022
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome
Data augmentation plays a key role in modern machine learning pipelines....
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04/28/2022
On the Normalizing Constant of the Continuous Categorical Distribution
Probability distributions supported on the simplex enjoy a wide range of...
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02/14/2022
Deep Ensembles Work, But Are They Necessary?
Ensembling neural networks is an effective way to increase accuracy, and...
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02/14/2022
Mapping Interstellar Dust with Gaussian Processes
Interstellar dust corrupts nearly every stellar observation, and account...
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12/07/2021
Scaling Structured Inference with Randomization
Deep discrete structured models have seen considerable progress recently...
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12/06/2021
Posterior Predictive Null Checks
Bayesian model criticism is an important part of the practice of Bayesia...
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07/01/2021
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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06/11/2021
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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06/02/2021
Rectangular Flows for Manifold Learning
Normalizing flows are invertible neural networks with tractable change-o...
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03/03/2021
Simulating time to event prediction with spatiotemporal echocardiography deep learning
Integrating methods for time-to-event prediction with diagnostic imaging...
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03/02/2021
Medical Imaging and Machine Learning
Advances in computing power, deep learning architectures, and expert lab...
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02/28/2021
Predicting post-operative right ventricular failure using video-based deep learning
Non-invasive and cost effective in nature, the echocardiogram allows for...
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02/12/2021
Bias-Free Scalable Gaussian Processes via Randomized Truncations
Scalable Gaussian Process methods are computationally attractive, yet in...
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11/10/2020
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning
Modern deep learning is primarily an experimental science, in which empi...
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03/11/2020
General linear-time inference for Gaussian Processes on one dimension
Gaussian Processes (GPs) provide a powerful probabilistic framework for ...
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02/20/2020
The continuous categorical: a novel simplex-valued exponential family
Simplex-valued data appear throughout statistics and machine learning, f...
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01/07/2020
Paraphrase Generation with Latent Bag of Words
Paraphrase generation is a longstanding important problem in natural lan...
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12/19/2019
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
The Gumbel-Softmax is a continuous distribution over the simplex that is...
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07/16/2019
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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03/18/2019
Approximating exponential family models (not single distributions) with a two-network architecture
Recently much attention has been paid to deep generative models, since t...
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03/06/2019
Deep Random Splines for Point Process Intensity Estimation
Gaussian processes are the leading class of distributions on random func...
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12/01/2018
A Probabilistic Model of Cardiac Physiology and Electrocardiograms
An electrocardiogram (EKG) is a common, non-invasive test that measures ...
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05/26/2018
Calibrating Deep Convolutional Gaussian Processes
The wide adoption of Convolutional Neural Networks (CNNs) in application...
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05/25/2018
Bayesian estimation for large scale multivariate Ornstein-Uhlenbeck model of brain connectivity
Estimation of reliable whole-brain connectivity is a crucial step toward...
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10/26/2017
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Many matching, tracking, sorting, and ranking problems require probabili...
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01/12/2017
Maximum Entropy Flow Networks
Maximum entropy modeling is a flexible and popular framework for formula...
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05/26/2016
Linear dynamical neural population models through nonlinear embeddings
A body of recent work in modeling neural activity focuses on recovering ...
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03/07/2016
Bayesian Learning of Kernel Embeddings
Kernel methods are one of the mainstays of machine learning, but the pro...
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02/22/2016
Preconditioning Kernel Matrices
The computational and storage complexity of kernel machines presents the...
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11/13/2015
Neuroprosthetic decoder training as imitation learning
Neuroprosthetic brain-computer interfaces function via an algorithm whic...
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12/16/2014
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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09/18/2012
Scaling Multidimensional Inference for Structured Gaussian Processes
Exact Gaussian Process (GP) regression has O(N^3) runtime for data size ...
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11/29/2011