
PhysicsInformed Neural Network for Modelling the Thermochemical Curing Process of CompositeTool Systems During Manufacture
We present a PhysicsInformed Neural Network (PINN) to simulate the ther...
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Finite mixture models are typically inconsistent for the number of components
Scientists and engineers are often interested in learning the number of ...
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Slice Sampling for General Completely Random Measures
Completely random measures provide a principled approach to creating fle...
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Truncated SelfProduct Measures in EdgeExchangeable Networks
Edgeexchangeable probabilistic network models generate edges as an i.i....
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Practical Posterior Error Bounds from Variational Objectives
Variational inference has become an increasingly attractive, computation...
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Local Exchangeability
Exchangeabilityin which the distribution of an infinite sequence is i...
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Sparse Variational Inference: Bayesian Coresets from Scratch
The proliferation of automated inference algorithms in Bayesian statisti...
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Universal Boosting Variational Inference
Boosting variational inference (BVI) approximates an intractable probabi...
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Reconstructing probabilistic trees of cellular differentiation from singlecell RNAseq data
Until recently, transcriptomics was limited to bulk RNA sequencing, obsc...
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Datadependent compression of random features for largescale kernel approximation
Kernel methods offer the flexibility to learn complex relationships in m...
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Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Bayesian inference typically requires the computation of an approximatio...
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Scalable Gaussian Process Inference with Finitedata Mean and Variance Guarantees
Gaussian processes (GPs) offer a flexible class of priors for nonparamet...
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Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Coherent uncertainty quantification is a key strength of Bayesian method...
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Automated Scalable Bayesian Inference via Hilbert Coresets
The automation of posterior inference in Bayesian data analysis has enab...
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Dynamic Clustering Algorithms via SmallVariance Analysis of Markov Chain Mixture Models
Bayesian nonparametrics are a class of probabilistic models in which the...
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Coresets for Scalable Bayesian Logistic Regression
The use of Bayesian methods in largescale data settings is attractive b...
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Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
Point cloud alignment is a common problem in computer vision and robotic...
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Streaming, Distributed Variational Inference for Bayesian Nonparametrics
This paper presents a methodology for creating streaming, distributed in...
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Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
This paper presents a novel algorithm, based upon the dependent Dirichle...
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Trevor Campbell
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