Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set? by Minyi Su (1425976)

“…In order to examine the true power of machine-learning algorithms in scoring function formulation, we have conducted a systematic study of six off-the-shelf machine-learning algorithms, including Bayesian Ridge Regression (BRR), Decision Tree (DT), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Linear Support Vector Regression (L-SVR), and Random Forest (RF). …”

Out-of-distribution prediction and performance of predictive models as a function of sample diversity. by Oualid Benkarim (9707986)

Performance of the three algorithms. by Juanjuan Lin (2096830)

“…An integrated framework based on a novel genetic algorithm and the Frank—Wolfe algorithm is designed to solve the stochastic model. …”

Recombinant mapping narrows region of interest to identify a loss-of-function allele of <i>mttp.</i> by McKenna Feltes (11242085)

Subjects:

Attack and defense behaviors with the sensory–motor algorithm between a single predator and a single prey. by Hiroyuki Ichijo (13891153)

Optimization outcome for the Rosenbrock function. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Optimization outcome for the Rastrigin function. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

2D Rastrigin function. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

2D Levy function. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

2D Rosenbrock function. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Optimization outcome for the Levy function. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics by Rama K. Vasudevan (1612174)

“…Polarization dynamics in ferroelectric materials are explored <i>via</i> automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. …”

Frequency of binding site usage as a function of binding site length. by Virgile Andreani (17087764)

Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic by NELSON L. DIAS (8078003)

“…These implementations need to utilize high-precision arithmetic, and are programmed in the Python programming language. A thorough comparison with existing R functions allows the identification of cases when the latter fail, and provide further guidance to their use.…”

Attack and defense behaviors between a single predator and a single prey using the sensory–motor algorithm. by Hiroyuki Ichijo (13891153)

Constructing the dynamical law. by Robert Marsland (8616483)

Definitions of global parameters used for constructing random ecosystems. by Robert Marsland (8616483)

Left: the algorithm’s best estimate of where to put breakpoints for each number of breakpoints considered. by Zhengtian Li (5076704)

Mean crossing time as a function of <i>α</i>. by Pratik Mullick (12851011)

Idea of the search for a domain/motif within the range from 1 to W positions (in the window) instead of an only predetermined position. by Alexander V. Spirov (11302085)