Gaussian process ≈ Gaussian process
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Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization Open
Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. Several techniques have been develo…
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Gaussian Process Regression for Materials and Molecules Open
We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the …
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Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series Open
The growing field of large-scale time domain astronomy requires methods for probabilistic data analysis that are computationally tractable, even with large data sets. Gaussian processes (GPs) are a popular class of models used for this pur…
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When Gaussian Process Meets Big Data: A Review of Scalable GPs Open
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 meanwhile, it poses challenges for the Gaussian process regression (GPR), a…
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A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery Open
Predicting future capacities and remaining useful life (RUL) with uncertainty quantification is a key but challenging issue in the applications of battery health diagnosis and management. This paper applies advanced machine-learning techni…
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To address surface reaction network complexity using scaling relations machine learning and DFT calculations Open
Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous …
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GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Open
Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware. We present an efficient and general approach to GP inference based on Blackbox M…
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Cautious Model Predictive Control Using Gaussian Process Regression Open
ISSN:1063-6536
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Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data Open
Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts. A central challenge is yield estimation, i.e., predicting crop yields before harvest. We introduce a scalable, accurate,…
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End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks Open
Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break befo…
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Machine Learning a General-Purpose Interatomic Potential for Silicon Open
The success of first-principles electronic-structure calculation for predictive modeling in chemistry, solid-state physics, and materials science is constrained by the limitations on simulated length scales and timescales due to the comput…
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Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling Open
Multi-fidelity modelling enables accurate inference of quantities of interest by synergistically combining realizations of low-cost/low-fidelity models with a small set of high-fidelity observations. This is particularly effective when the…
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A Case Study Competition Among Methods for Analyzing Large Spatial Data Open
Supplementary materials for this article are available at 10.1007/s13253-018-00348-w.
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A Tutorial on Bayesian Optimization Open
Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic no…
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Physics-Informed Neural Networks for Cardiac Activation Mapping Open
A critical procedure in diagnosing atrial fibrillation is the creation of electro-anatomic activation maps. Current methods generate these mappings from interpolation using a few sparse data points recorded inside the atria; they neither i…
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Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries Open
Accurate on-board capacity estimation is of critical importance in\nlithium-ion battery applications. Battery charging/discharging often occurs\nunder a constant current load, and hence voltage vs. time measurements under\nthis condition m…
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Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries Open
Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This paper derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regres…
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Safe Model-based Reinforcement Learning with Stability Guarantees Open
Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. However, to find optimal policies, most reinforcement learning algorithms explore all possible actions, which may be harmful for real-world…
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Deep Neural Networks as Gaussian Processes Open
It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width. This correspondence enables exact Bayesian…
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ACN-Data Open
We are releasing ACN-Data, a dynamic dataset of workplace EV charging which currently includes over 30,000 sessions with more added daily. In this paper we describe the dataset, as well as some interesting user behavior it exhibits. To dem…
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FCHL revisited: Faster and more accurate quantum machine learning Open
We introduce the FCHL19 representation for atomic environments in molecules or condensed-phase systems. Machine learning models based on FCHL19 are able to yield predictions of atomic forces and energies of query compounds with chemical ac…
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Deep Neural Networks as Gaussian Processes Open
It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width. This correspondence enables exact Bayesian…
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GPflow: A Gaussian process library using TensorFlow Open
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides co…
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MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for\n Behavior Prediction Open
Predicting human behavior is a difficult and crucial task required for motion\nplanning. It is challenging in large part due to the highly uncertain and\nmulti-modal set of possible outcomes in real-world domains such as autonomous\ndrivin…
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Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection Open
Early detection of incipient faults in industrial processes is increasingly becoming important, as these faults can slowly develop into serious abnormal events, an emergency situation, or even failure of critical equipment. Multivariate st…
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Safe controller optimization for quadrotors with Gaussian processes Open
One of the most fundamental problems when designing controllers for dynamic\nsystems is the tuning of the controller parameters. Typically, a model of the\nsystem is used to obtain an initial controller, but ultimately the controller\npara…
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Reduced order modeling for nonlinear structural analysis using Gaussian process regression Open
A non-intrusive reduced basis (RB) method is proposed for parametrized nonlinear structural analysis undergoing large deformations and with elasto-plastic constitutive relations. In this method, a reduced basis is constructed from a set of…
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Rare Event Estimation Using Polynomial-Chaos Kriging Open
Structural reliability analysis aims at computing the probability of failure of systems whose performance may be assessed by using complex computational models (e.g., expensive-to-run finite-element models). A direct use of Monte Carlo sim…
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FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing Open
In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods. We first review (1) state-of-the-art algorithms…
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Inferring probabilistic stellar rotation periods using Gaussian processes Open
Variability in the light curves of spotted, rotating stars is often\nnon-sinusoidal and quasi-periodic --- spots move on the stellar surface and\nhave finite lifetimes, causing stellar flux variations to slowly shift in\nphase. A strictly …