Titlebook: Advances in Artificial Intelligence; Selected Papers from Katsutoshi Yada,Daisuke Katagami,Hisashi Kashima Conference proceedings 2021 The

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https://doi.org/10.1007/978-3-030-11726-9uced by up?to 80% compared to that of the original size with only a 11% degradation at worst performance in our final lightweight detector model for image matching tasks. Our study indicates that the modern convolution techniques originally proposed for small image classification models can be effec

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Designing Lightweight Feature Descriptor Networks with Depthwise Separable Convolution,978-3-8348-2085-3

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2194-5357 n additional to artificial intelligence. We are extremely pleased to publish this collection of papers as the research results of our international sessions.978-3-030-73112-0978-3-030-73113-7Series ISSN 2194-5357 Series E-ISSN 2194-5365

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Identifying Snowfall Clouds at Syowa Station, Antarctica via a Convolutional Neural Network,design intricacies and considerations. Its audience includes undergraduate and graduate students with a specific interest in RF/optoelectronic receiver technology, along with researchers and engineers engaged i978-981-97-0883-3978-981-97-0881-9

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A Node Classification Approach for Dynamically Extracting the Structures of Online Discussions,ranging from 1?μm to full wafer thickness (725?μm), in-plane sizes spanning from nanometers to centimeters, ratios between etch depth and feature width reaching up to 70:1 – that is what high Aspect Ratio (AR) means. The silicon patterning must be anisotropic to guarantee a tight CD control, perpend

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