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www.mdpi.com | ||
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www.paperdigest.org
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| | | | | Download NIPS-2021-Paper-Digests.pdf- Highlights of all NeurIPS-2021 papers. Readers can choose to read all these highlights on our console, which allows users to filter out papers using keywords and find related papers, patents, etc. In addition, we identified a large number of papers that have pub | |
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www.frontiersin.org
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| | | | | List of all Research Topics published by Frontiers Media SA. Frontiers' Research Topics are peer-reviewed article collections around themes of cutting-edge research. Defined, managed, and led by re... | |
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gmd.copernicus.org
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| | | | | Abstract. Forecasting heavy precipitation accurately is a challenging task for most deep learning (DL)-based models. To address this, we present a novel DL architecture called multi-scale feature fusion (MFF) that can forecast precipitation with a lead time of up to 3?h. The MFF model uses convolution kernels with varying sizes to create multi-scale receptive fields. This helps to capture the movement features of precipitation systems, such as their shape, movement direction, and speed. Additionally, the... | |
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vxlabs.com
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| | | I have recently become fascinated with (Variational) Autoencoders and with PyTorch. Kevin Frans has a beautiful blog post online explaining variational autoencoders, with examples in TensorFlow and, importantly, with cat pictures. Jaan Altosaar's blog post takes an even deeper look at VAEs from both the deep learning perspective and the perspective of graphical models. Both of these posts, as well as Diederik Kingma's original 2014 paper Auto-Encoding Variational Bayes, are more than worth your time. | ||
