The Latest Research in Convolutional Neural Networks

Recently published open access research in convolutional neural networks   View in Browser SUBMIT AN ARTICLE MANAGE MY ALERTS ABOUT THE JOURNAL APL Machine Learning The Latest in Convolutional Neural Networks APL Machine Learning publishes insightful perspectives and groundbreaking research in convolutional neural networks. Convolutional Neural Networks (CNNs) are a key component of machine learning research, particularly within the field of deep learning. Their ability to automatically and adaptively learn spatial hierarchies of features from input data has made them indispensable in various applications, especially in computer vision. Check out this exciting selection of articles published in APL Machine Learning to learn about the latest advances in the field of convolutional neural networks.   Get Your Work Read As an open access journal, articles in APL Machine Learning are always available to read, download, and share—no subscription needed. SUBMIT AN ARTICLE > Waveform retrieval for ultrafast applications based on convolutional neural networks Najd Altwaijry, Ryan Coffee, Matthias F. Kling READ MORE > Determining the density and spatial descriptors of atomic scale defects of 2H–WSe2 with ensemble deep learning Darian Smalley, Stephanie D. Lough, Luke N. Holtzman, Madisen Holbrook, et al. READ MORE > 3D–2D neural nets for phase retrieval in noisy interferometric imaging Andrew H. Proppe, Guillaume Thekkadath, Duncan England, Philip J. Bustard, et al. READ MORE > Machine learning assisted search for Fe–Co–C ternary compounds with high magnetic anisotropy Weiyi Xia, Masahiro Sakurai, Timothy Liao, Renhai Wang, et al. READ MORE > Sim2Real in reconstructive spectroscopy: Deep learning with augmented device-informed data simulation Jiyi Chen, Pengyu Li, Yutong Wang, Pei-Cheng Ku, et al. READ MORE > Large-scale-aware data augmentation for reduced-order models of high-dimensional flows Philipp Teutsch, Mohammad Sharifi Ghazijahani, Florian Heyder, Christian Cierpka, et al. READ MORE > Deep-learning design of electronic metasurfaces in graphene for quantum control and Dirac electron holography Chen-Di Han, Li-Li Ye, Zin Lin, Vassilios Kovanis, et al. READ MORE > Heterogeneous reinforcement learning for defending power grids against attacks Mohammadamin Moradi, Shirin Panahi, Zheng-Meng Zhai, Yang Weng, et al. READ MORE > Accelerating simulations of strained-film growth by deep learning: Finite element method accuracy over long time scales Daniele Lanzoni, Fabrizio Rovaris, Luis Martín-Encinar, Andrea Fantasia, et al. READ MORE > Elastic constants from charge density distribution in FCC high-entropy alloys using CNN and DFT Hossein Mirzaee, Ramin Soltanmohammadi, Nathan Linton, Jacob Fischer, et al. READ MORE > A systematic dataset generation technique applied to data-driven automotive aerodynamics Mark Benjamin, Gianluca Iaccarino READ MORE > Deep learning for quantitative dynamic fragmentation analysis Erwin Cazares, Brian E. Schuster READ MORE > Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning Teemu Koivisto, Marcin Mińkowski, Lasse Laurson READ MORE > Symmetry constrained neural networks for detection and localization of damage in metal plates James Amarel, Christopher Rudolf, Athanasios Iliopoulos, John G. Michopoulos, et al. READ MORE > Scalable recurrence graph network for stratifying RhoB texture dynamics in rectal cancer biopsies Tuan D. Pham READ MORE > Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system Philipp Teutsch, Philipp Pfeffer, Mohammad Sharifi Ghazijahani, Christian Cierpka, et al. READ MORE > Environment model construction toward auto-tuning of quantum dot devices based on model-based reinforcement learning Chihiro Kondo, Raisei Mizokuchi, Jun Yoneda, Tetsuo Kodera READ MORE > Research and Integrity We believe there has never been a more important time to stand firm in support of independent, open science. We work hand-in-hand with the global scientific community to safeguard the integrity of science. 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