Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf by Santiago Hernández-Orozco (5070209)

“…We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. …”

datasheet2_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.zip by Santiago Hernández-Orozco (5070209)

“…We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. …”

datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf by Santiago Hernández-Orozco (5070209)

“…We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. …”

datasheet2_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.zip by Santiago Hernández-Orozco (5070209)

“…We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. …”

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

Comparison of algorithms in two cases. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Flow of the NSGA-II algorithm. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

Subjects:

An expectation-maximization algorithm for finding noninvadable stationary states. by Robert Marsland (8616483)

Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”

Experimental results for all algorithms on benchmark functions. by Shuhan Du (13955497)

Copper exposure in <i>ATP7B</i> Knock-out HepG2 cells overexpressed with the plasmid containing wildtype and mutant <i>ATP7B</i> gene. by Aastha Vatsyayan (9981916)

Subjects:

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Final assignment algorithm. by Negin Alisoltani (11949357)

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”

Average runtime for all algorithms on benchmark functions (in second). by Shuhan Du (13955497)

ROI setting diagram for AI calculation and optimization verification results. by Tsutomu Gomi (7382183)

Subjects: