Engineering and Technologynpj Computational Materials
Validating neural networks for spectroscopic classification on a universal synthetic dataset
J. Schuetzke, N. J. Szymanski, et al.
Explore groundbreaking research by Jan Schuetzke, Nathan J. Szymanski, and Markus Reischl, who developed a universal synthetic dataset for spectroscopic data classification. Their study achieved over 98% accuracy with various neural network architectures, revealing important insights into model performance and classification challenges.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Medicine and Health
Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections
Y. Kim, S. Kim, et al.
Physics
Deep quantum neural networks on a superconducting processor
X. Pan, Z. Lu, et al.
Biology
A genetically encoded tool for reconstituting synthetic modulatory neurotransmission and reconnect neural circuits in vivo
J. D. Hawk, E. M. Wisdom, et al.
Engineering and Technology
A framework for the general design and computation of hybrid neural networks
R. Zhao, Z. Yang, et al.
