High bias machine learning for antineutrino-based safeguards for small reactors

A high-bias, low-variance introduction to Machine Learning for physicists

Physics Reports ◽

10.1016/j.physrep.2019.03.001 ◽

2019 ◽

Vol 810 ◽

pp. 1-124 ◽

Cited By ~ 136

Author(s):

Pankaj Mehta ◽

Marin Bukov ◽

Ching-Hao Wang ◽

Alexandre G.R. Day ◽

Clint Richardson ◽

...

Keyword(s):

Machine Learning ◽

High Bias

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Comparison of Random Forest and Gradient Boosting Fingerprints to Enhance an Outdoor Radio-frequency Localization System

10.21203/rs.3.rs-108739/v1 ◽

2020 ◽

Author(s):

Marcelo Nogueira de Sousa ◽

Ricardo Sant'Ana ◽

Riegel P. Fernandes ◽

Julio Cesár Duarte ◽

José A. Aploinário ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Radio Frequency ◽

Simulated Data ◽

Gradient Boosting ◽

Learning Tools ◽

Learning Framework ◽

Error Mitigation ◽

High Bias ◽

Outdoor Localization

Abstract Machine Learning framework adds a new dimension to the localization estimation problem; it tries to find the most likely position using processed features in a radio map. This paper compares the performance of two machine learning tools, Random Forest (RF) and XGBoost, in exploiting the multipath information for outdoor localization problem. The investigation was carried out in a noisy outdoor scenario, where non-line-of-sight between target and sensors may affect the location of a radio-frequency emitter strongly. It is possible to improve the position system performance by using fingerprints techniques that employ multipath information in a Machine Learning framework, which operate a dataset generated by ray-tracing simulation. Usually, real measurements produce the fingerprints localization features, and there is mismatching with the simulated data. Another drawback of NLOS features extraction is the noise level that occurs in position processing. Random Forest algorithm uses fully grown decision trees to classify possible emitter position, trying to achieve error mitigation by reducing variance. On the other hand, XGBoost approach uses weak learners, defined by high bias and low variance. The results of the simulation performed aims to be used as a design parameter to perform hyperparameter refinements in similar multipath localization problems.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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