Algorithm description and the effects of replay and forgetting on model performance. by Georgy Antonov (11938961)

“…Right: with MB forgetting (controlled by MB forgetting rate, <i>ϕ</i>^<i>MB</i>), the algorithm’s estimate of reward becomes an expectation of the reward function under its state-transition model. …”

The impact of extrinsic noise sources on the measured SNR is plotted for both CW and TG against varying optical filter magnitudes. by Nicholas H. Vitale (20469289)

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Machine learning algorithm parameters. by Namal Rathnayake (15361124)

“…Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). …”

Similarity between the allegiance matrices of the multiplex SC-FC graph, the FC graph alone, and the SC graph alone, as a function of the topological scale for the group. by Arian Ashourvan (6685232)

Trainable parameters for different algorithms. by Anwar Hossain Efat (19942283)

Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

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Demonstration of the combinatorial Hill functions (A and B), and the regulation function (C and D) under the regulation of an activator and a repressor. by Ruihao Li (2908991)

Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

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Globus Compute: Fire-and-Forget Federated Function-as-a-Service by Kyle Chard (5131859)

Objective function for pressure control system. by Fazal E. Nasir (14992506)

Constructing the dynamical law. by Robert Marsland (8616483)

Dataset for 'An Automated Workflow of Deep Learning Ensemble Model with Principal Component Analysis for Enhanced Material Characterisation in Energy Dispersive Spectroscopy' by Fanzhi Su (21005174)

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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It includes raw data extracted from graphical experimental data sets presented in [22,23] for both P2X receptors and used in the fitting process. by Alireza Poshtkohi (11632250)

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The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . …”

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

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Analytic Gradients for Density Fitting MP2 Using Natural Auxiliary Functions by Klára Petrov (19251734)

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Variations of loss functions versus epoch number. by Xueliang Guo (4797057)

Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…The most widely employed tool to perform this task is MODELLER. This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. …”

Rosetta energy functions for membrane proteins cannot identify the inward-facing conformation of PfMATE. by Diego del Alamo (9925083)