Compressive Sensing for Dynamic XRF Scanning
George Kourousias, Fulvio Billè, Roberto Borghes et al.
Data Analysis papers — page 35
physics.data-anBrowse archive
HEPLike: an open source framework for experimental likelihood evaluation
Jihyun Bhom, Marcin ChrzaszczUsing Curvature to Select the Time Lag for Delay Reconstruction
Varad Deshmukh, Elizabeth Bradley, Joshua Garland et al.Multiplex Recurrence Networks
Deniz Eroglu, Norbert Marwan, Martina Stebich et al.Reducing network size and improving prediction stability of reservoir computing
Alexander Haluszczynski, Jonas Aumeier, Joschka Herteux et al.Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principles
Kyle Sprague, Juan Carrasquilla, Steve Whitelam et al.The statistical physics of discovering exogenous and endogenous factors in a chain of events
Shinsuke Koyama, Shigeru ShinomotoAutomated detector simulation and reconstruction parametrization using machine learning
D. Benjamin, S. V. Chekanov, W. Hopkins et al.Reconstructing particle number distributions with convoluting volume fluctuations
ShinIchi Esumi, Kana Nakagawa, Toshihiro NonakaModeling Aerial Gamma-Ray Backgrounds using Non-negative Matrix Factorization
M. S. Bandstra, T. H. Y. Joshi, K. J. Bilton et al.Bryan's Maximum Entropy Method -- diagnosis of a flawed argument and its remedy
Alexander RothkopfUsing machine learning to separate hadronic and electromagnetic interactions in the GlueX forward calorimeter
Rebecca Barsotti, Matthew R. ShepherdFast Time Series Detrending with Applications to Heart Rate Variability Analysis
M. AndrecutStudy of a data analysis method for the angle resolving silicon telescope
P. Žugec, M. Barbagallo, J. Andrzejewski et al.Uncertainty Quantification of Mode Shape Variation Utilizing Multi-Level Multi-Response Gaussian Process
Kai Zhou, Jiong TangObject condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data
Jan KieselerOn the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case
Giles Chatham StrongCritical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach
Thanh Dung Le, Rita Noumeir, Huu Luong Quach et al.Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic Data
Han Peng, Xiang Gao, Yu He et al.Deconvolution of the High Energy Particle Physics Data with Machine Learning
Bora Işıldak, Alper Hayreter, Aidan R. WiederholdWISDoM: characterizing neurological timeseries with the Wishart distribution
Carlo Mengucci, Daniel Remondini, Gastone Castellani et al.BLUE: combining correlated estimates of physics observables within ROOT using the Best Linear Unbiased Estimate method
Richard NisiusFast Algorithm for computing a matrix transform used to detect trends in noisy data
D. J. Kestner, G. R. Ierley, A. B. KostinskiA note on causation versus correlation
X. San Liang, Xiuqun YangVariational Dropout Sparsification for Particle Identification speed-up
Artem Ryzhikov, Denis Derkach, Mikhail Hushchyn