Results 51 to 60 of about 6,478,030 (244)

Training Neural Networks by Time-Fractional Gradient Descent

Axioms, 2022
Motivated by the weighted averaging method for training neural networks, we study the time-fractional gradient descent (TFGD) method based on the time-fractional gradient flow and explore the influence of memory dependence on neural network training. The
Jingyi Xie, Sirui Li
doaj   +1 more source

Solving Fractional Differential Equations by Using Triangle Neural Network

Journal of Function Spaces, 2021
In this paper, numerical methods for solving fractional differential equations by using a triangle neural network are proposed. The fractional derivative is considered Caputo type.
Feng Gao, Yumin Dong, Chunmei Chi
doaj   +1 more source

Forecasting Air Quality Index Using an Ensemble of Artificial Neural Networks and Regression Models

Journal of Intelligent Systems, 2017
Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality
Sankar Ganesh S., Arulmozhivarman Pachaiyappan, Tatavarti Rao   +2 more
doaj   +1 more source

AG-SGD: Angle-Based Stochastic Gradient Descent

IEEE Access, 2021
In the field of neural network, stochastic gradient descent is often employed as an effective method of accelerating the result's convergence. Generating the new gradient from the past gradient is a common method adopted by many existing optimization ...
Chongya Song, Alexander Pons, Kang Yen
doaj   +1 more source

Emerging experimental and computational methods for studying redox‐regulated structural transitions

FEBS Letters, EarlyView.
Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass, Dana Reichmann, Gábor Erdős   +2 more
wiley   +1 more source

Homogeneous ridge polynomial neural network training by PS module asynchronous gradient method

Alexandria Engineering Journal
To address the issue that the passive increase in network order resulting from the rise of the Pi-Sigma (PS) modules for the ridge polynomial neural network (RPNN) adversely affects network performance, we propose a homogeneous ridge polynomial neural ...
Tangwang Sun, Yan Xiong, Jiajia Hu
doaj   +1 more source

Prospecting the protein design landscape

FEBS Letters, EarlyView.
This review outlines the current state of various protein design approaches. We discuss the current possibilities enabled by recently released tools, highlight future avenues to pursue in protein design, and underscore the crucial role of key databases and resources for successful protein design workflows.
Jakob R. Riccabona, Katharina T. Stonig, Jens Meiler, Clara T. Schoeder, Monica L. Fernández‐Quintero   +4 more
wiley   +1 more source

Metastatic niche shaped by host factors influences disseminated cancer cell fate

FEBS Letters, EarlyView.
Metastasis is shaped not only by cancer cells but also by the environments they encounter. This review explores how factors such as aging, diet, the microbiome, lifestyle, and environmental exposures remodel organ‐specific niches in the lung, liver, bone, and brain, influencing where metastatic cells survive, remain dormant, or grow, and ultimately ...
Gwennan Delyth Ward, Álvaro Ruiz‐Tabas, Patricia Altea‐Manzano   +2 more
wiley   +1 more source

Global crustal thickness from neural network inversion of surface wave data [PDF]

, 2007
We present a neural network approach to invert surface wave data for a global model of crustal thickness with corresponding uncertainties. We model the a posteriori probability distribution of Moho depth as a mixture of Gaussians and let the various ...
Andrew Curtis, Meier, U., Curtis, A.; id_orcid, Trampert, J., Ueli Meier, Jeannot Trampert   +5 more
core   +1 more source

Going viral − Virus‐targeting nanobodies for research, diagnostics and therapy

FEBS Letters, EarlyView.
The increasing prevalence of viral diseases represents a major threat for global human health. Nanobodies, small binding fragments derived from camelid heavy‐chain antibodies, offer versatile tools for viral research, diagnostics, and antiviral therapy.
Domenic Ebert, Ulrich Rothbauer
wiley   +1 more source