An application of hierarchical Gaussian processes to the detection of anomalies in star light curves

Twomey, Niall; Chen, Haoyan; Diethe, Tom; Flach, Peter A.

doi:10.1016/j.neucom.2018.11.087

Cited by 9 publications

(13 citation statements)

References 27 publications

(44 reference statements)

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“…To achieve this, we used HLGPR. This builds upon prior work using hierarchical GPs to directly model observed time series, both in MEG work ( 13 ) and other fields ( 58 , 59 ), and provides an intuitive approach to modeling time series data while accounting for its covariance structure. This is particularly appealing for the analysis performed here, as directly accounting for covariance allows us to circumvent multiple comparison corrections across time points, which presents a problem when identifying significant effects if we expect these effects to be brief in their duration.…”

Section: Methodsmentioning

confidence: 99%

“…This approach allows us to take advantage of the benefits of hierarchical models in terms of aiding parameter estimation when using data that are limited in quantity and quality, as is typical of neuroimaging data. To achieve this, we turn to prior work on hierarchical GPs ( 58 , 59 ) and use a hierarchical mean and covariance structure to define the GPs used to represent regression coefficients. For each regressor, we first define a constant mean group-level GP representing the group mean time-varying regression weights.…”

Section: Methodsmentioning

confidence: 99%

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Model-based aversive learning in humans is supported by preferential task state reactivation

et al. 2021

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Harm avoidance is critical for survival, yet little is known regarding the neural mechanisms supporting avoidance in the absence of trial-and-error experience. Flexible avoidance may be supported by a mental model (i.e., model-based), a process for which neural reactivation and sequential replay have emerged as candidate mechanisms. During an aversive learning task, combined with magnetoencephalography, we show prospective and retrospective reactivation during planning and learning, respectively, coupled to evidence for sequential replay. Specifically, when individuals plan in an aversive context, we find preferential reactivation of subsequently chosen goal states. Stronger reactivation is associated with greater hippocampal theta power. At outcome receipt, unchosen goal states are reactivated regardless of outcome valence. Replay of paths leading to goal states was modulated by outcome valence, with aversive outcomes associated with stronger reverse replay than safe outcomes. Our findings are suggestive of avoidance involving simulation of unexperienced states through hippocampally mediated reactivation and replay.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Model-based aversive learning in humans is supported by preferential task state reactivation

et al. 2021

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“…To achieve this, we used hierarchical Bayesian latent Gaussian process regression (HLGPR). This builds upon prior work using hierarchical Gaussian processes (GPs) to 607 directly model observed timeseries, both in MEG work 13 and other fields 60,61 , and provides an intuitive approach to modelling timeseries data while accounting for its covariance structure. This is particularly appealing for the analysis performed here, as directly accounting for covariance allows us to circumvent multiple comparison corrections across time points, which presents a problem when identifying significant effects if we expect these effects to be brief in their duration.…”

Section: Statistical Analysis Of Sequenceness Datamentioning

confidence: 99%

“…This approach allows us to take advantage of the benefits of hierarchical models in terms of aiding parameter estimation when using data that is limited in quantity and quality, as is typical of neuroimaging data. To achieve this, we turn to prior work on hierarchical GPs 60,61 , and use a hierarchical mean and covariance structure to define the GPs used to represent regression coefficients. For each regressor, we first define a constant-mean group-level GP representing the group mean time-varying regression weights.…”

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confidence: 99%

Model-based aversive learning in humans is supported by preferential task state reactivation

Wise

Liu

Chowdhury

et al. 2020

Preprint

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Harm avoidance is critical for survival, yet little is known regarding the underlying neural mechanisms supporting avoidance when we cannot rely on direct trial and error experience. Neural reactivation, and sequential replay, have emerged as potential candidate mechanisms. Here, during an aversive learning task, in conjunction with magnetoencephalography, we show prospective and retrospective reactivation for planning and learning respectively, coupled to evidence for sequential replay. Specifically, when subjects plan in an aversive context, we find preferential reactivation of subsequently chosen goal states and sequential replay of the preceding path. This reactivation was associated with greater hippocampal theta power. At outcome receipt, unchosen goal states are reactivated regardless of outcome valence. However, replay of paths leading to goal states was directionally modulated by outcome valence, with aversive outcomes leading to stronger reverse replay compared to safe outcomes. Our findings suggest that avoidance behaviour involves simulation of alternative future and past outcome states through hippocampally-mediated reactivation and replay.

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“…It has strong reasoning ability, good interpretability, and exhibits strong adaptability in case of issues such as high dimensions, small samples, and nonlinearity. Furthermore, it has the advantages of no function constraint, adaptive acquisition of model parameters, and probabilistic output, which is a research hotspot in the field of machine learning [18]. The conjugate gradient method is generally used to determine the optimal hyper-parameters of the Gauss process [19].…”

Section: Introductionmentioning

confidence: 99%

Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm

Fan

et al. 2019

PLoS ONE

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An algorithm to predict train wheel diameter based on Gaussian process regression (GPR) optimized using a fast simulated annealing algorithm (FSA-GPR) is proposed in this study to address the problem of dynamic decrease in wheel diameter with increase in mileage, which affects the measurement accuracy of train speed and location, as well as the hyper-parameter problem of the GPR in the traditional conjugate gradient algorithm. The algorithm proposed as well as other popular algorithms in the field, such as the traditional GPR algorithm, and GPR algorithms optimized using the artificial bee colony algorithm (ABC-GPR) or genetic algorithm (GA-GPR), were used to predict the wheel diameter of a DF11 train in a section of a railway during a period of major repairs. The results predicted by FSA-GPR was compared with other three algorithms as well as the real measured data from RMSE, MAE, R2 and Residual value. And the comparisons showed that the predictions obtained from the GPR optimized using FSA algorithm were more accurate than those based on the others. Therefore, this algorithm can be incorporated into the vehicle-mounted speed measurement module to automatically update the value of wheel diameter, thereby substantially reducing the manual work entailed therein and improving the effectiveness of measuring the speed and position of the train.

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An application of hierarchical Gaussian processes to the detection of anomalies in star light curves

Cited by 9 publications

References 27 publications

Model-based aversive learning in humans is supported by preferential task state reactivation

Model-based aversive learning in humans is supported by preferential task state reactivation

Model-based aversive learning in humans is supported by preferential task state reactivation

Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm

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