A new technical paper titled “Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning” was published by researchers at IBM TJ Watson Research Center and Rensselaer Polytechnic Institute.
Find the technical paper here. Published November 2022. Open Access.
Scott D. Halle, Derren N. Dunn, Allen H. Gabor, Max O. Bloomfield, and Mark Shephard “Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning,” Journal of Micro/Nanopatterning, Materials, and Metrology 21(4), 041604 (8 November 2022). https://doi.org/10.1117/1.JMM.21.4.041604.
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