A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

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Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

Comparison of deconvolution and optimization algorithms on a batch of data. by Ali-Kemal Aydin (10968731)

“…Output is given by the vascular response, measured as the change in speed of red blood cells flowing inside a capillary proximal to the recorded neuronal activation (in yellow, right panel). Both experimental data have been resampled at 50ms and used to compute a set of TFs (in orange) either with direct deconvolution approaches (Fourier or Toeplitz methods, middle-upper panel TFs) or with 1-Γ function optimization performed by 3 different algorithms (middle-lower panel TFs). …”

The benchmark functions. by Shuhui Yu (1535500)

Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. by María Victoria Waks-Serra (21578611)

Gillespie algorithm simulation parameters. by Nicholas H. Vitale (20469289)

“…<div><p>We present a model for the noise and inherent stochasticity of fluorescence signals in both continuous wave (CW) and time-gated (TG) conditions. …”

Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

“…Our analysis pipeline included pre-processing steps, feature extraction from both time and frequency domains, a voting algorithm for selecting features, and model training and validation. …”

Algorithm process of BPNN. by Xiaoxiang Cao (12644851)

Automatic cropping algorithm. by Thomas W Georgi (12427189)

As for Fig 2, we present failure rates as a function of the cohort size (vertical axis) versus the number of distractors (horizontal axis), for the Smyth and McClave baseline algor... by Daniela Huppenkothen (9174507)

“…<p>We explore the behaviour of the baseline algorithm both in terms of whether it can recover maximally diverse cohorts (upper four panels), and whether it can recover imbalanced cohorts. …”

Ridge wave transformation flowchart. by Bingbing Li (461702)

“…Unlike traditional threshold functions, the improved threshold function is a continuous function that can avoid the pseudo Gibbs effect after image denoising and improve image quality. …”

Application of the algorithm to unseen data. Presynaptic neurons labelled by rabies viral injection into layer 2/3 primary visual cortex in Penk-Cre mice. by Adam L. Tyson (10892822)

Algorithm. by Daniel Sanchez-Taltavull (11654630)

Average function evaluation times of the three optimization algorithms. by Shuhui Yu (1535500)

Excited-State DMRG Made Simple with FEAST by Alberto Baiardi (1914601)

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Excited-State DMRG Made Simple with FEAST by Alberto Baiardi (1914601)

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Loss function value changes in CNN training changes with epochs for both training dataset and testing dataset. by Jiaxi Wang (545353)

Block ridge wave denoising flowchart. by Bingbing Li (461702)

“…Unlike traditional threshold functions, the improved threshold function is a continuous function that can avoid the pseudo Gibbs effect after image denoising and improve image quality. …”

Implementation algorithm. by Michael A. Colman (7163735)

Meta data for selected plots of "Optimal sampling of tensor networks targeting wave function’s fast decaying tails" by Marco Ballarin (17780767)