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08/28/2023
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
In real-world scenarios like traffic and energy, massive time-series dat...
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05/12/2023
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation
The Schrödinger bridge problem (SBP) is gaining increasing attention in ...
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02/28/2023
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
Physics-informed neural networks (PINNs) as a means of solving partial d...
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02/07/2023
Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations
In this paper, we present a machine learning method for the discovery of...
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10/23/2022
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) are emerging as popular mesh-fr...
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10/14/2022
A Kernel Approach for PDE Discovery and Operator Learning
This article presents a three-step framework for learning and solving pa...
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07/08/2022
Nonparametric Embeddings of Sparse High-Order Interaction Events
High-order interaction events are common in real-world applications. Lea...
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07/06/2022
Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition
Tensor decomposition is a fundamental framework to analyze data that can...
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07/01/2022
Infinite-Fidelity Coregionalization for Physical Simulation
Multi-fidelity modeling and learning are important in physical simulatio...
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06/07/2022
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm
Pruning techniques have been successfully used in neural networks to tra...
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03/09/2022
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
Neural networks tend to achieve better accuracy with training if they ar...
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02/24/2022
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Physics modeling is critical for modern science and engineering applicat...
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12/17/2021
GP-HMAT: Scalable, O(nlog(n)) Gaussian Process Regression with Hierarchical Low-Rank Matrices
A Gaussian process (GP) is a powerful and widely used regression techniq...
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10/26/2021
Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach
Physics-informed neural networks (PINNs) as a means of discretizing part...
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10/19/2021
Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes
We propose a nonparametric factorization approach for sparsely observed ...
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10/16/2021
Meta-Learning with Adjoint Methods
Model Agnostic Meta-Learning (MAML) is widely used to find a good initia...
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09/02/2021
Characterizing possible failure modes in physics-informed neural networks
Recent work in scientific machine learning has developed so-called physi...
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06/25/2021
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)
Multifidelity simulation methodologies are often used in an attempt to j...
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06/18/2021
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
Bayesian optimization (BO) is a powerful approach for optimizing black-b...
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12/02/2020
Deep Multi-Fidelity Active Learning of High-dimensional Outputs
Many applications, such as in physical simulation and engineering design...
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10/10/2020
Block-term Tensor Neural Networks
Deep neural networks (DNNs) have achieved outstanding performance in a w...
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07/14/2020
Streaming Probabilistic Deep Tensor Factorization
Despite the success of existing tensor factorization methods, most of th...
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07/06/2020
Multi-Fidelity Bayesian Optimization via Deep Neural Networks
Bayesian optimization (BO) is a popular framework to optimize black-box ...
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06/08/2020
Physics Regularized Gaussian Processes
We consider incorporating incomplete physics knowledge, expressed as dif...
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06/08/2020
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation
The key task of physical simulation is to solve partial differential equ...
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03/25/2020
Scalable Variational Gaussian Process Regression Networks
Gaussian process regression networks (GPRN) are powerful Bayesian models...
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02/06/2020
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications
Despite the wide implementation of machine learning (ML) techniques in t...
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10/27/2019
Conditional Expectation Propagation
Expectation propagation (EP) is a powerful approximate inference algorit...
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10/16/2019
Deep Coregionalization for the Emulation of Spatial-Temporal Fields
Data-driven surrogate models are widely used for applications such as de...
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12/14/2017
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. H...
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04/22/2017
Asynchronous Distributed Variational Gaussian Processes for Regression
Gaussian processes (GPs) are powerful non-parametric function estimators...
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04/27/2016
Distributed Flexible Nonlinear Tensor Factorization
Tensor factorization is a powerful tool to analyse multi-way data. Compa...
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11/12/2013
DinTucker: Scaling up Gaussian process models on multidimensional arrays with billions of elements
Infinite Tucker Decomposition (InfTucker) and random function prior mode...
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04/26/2013
