IEEE

Parametric Activation Functions for Neural Networks: A Tutorial Survey

Abstract: Activation functions are pivotal in neural networks, determining the output of each neuron. Traditionally, functions like sigmoid and ReLU have been static and deterministic. However, the ...
C&EN

Development of Coarse-Grained Lipid Force Fields Based on a Graph Neural Network

Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong China Department of Physics, City University of Hong Kong, Kowloon 999077, Hong Kong China ...
GitHub

Graph Neural Network for Heat Transfer Problem

[1] F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, and G. Monfardini. The graph neural network model. IEEE Transactions on Neural Networks, 20(1):61 80, 2009.
IEEE

A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations

Abstract: Modern deep neural networks, particularly recent large language models, come with massive model sizes that require significant computational and storage ...
GitHub

tutorial_pinn_to_solve_hde

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C&EN

Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations ...