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Titlebook: Advances in Artificial Intelligence; 33rd Canadian Confer Cyril Goutte,Xiaodan Zhu Conference proceedings 2020 Springer Nature Switzerland

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發(fā)表于 2025-3-21 17:34:45 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Advances in Artificial Intelligence
期刊簡稱33rd Canadian Confer
影響因子2023Cyril Goutte,Xiaodan Zhu
視頻videohttp://file.papertrans.cn/147/146727/146727.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Advances in Artificial Intelligence; 33rd Canadian Confer Cyril Goutte,Xiaodan Zhu Conference proceedings 2020 Springer Nature Switzerland
影響因子This book constitutes the refereed proceedings of the 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, which was planned to take place in Ottawa, ON, Canada. Due to the COVID-19 pandemic, however, it was held virtually during?May 13–15, 2020..The 31 regular papers and 24 short papers presented together with 4 Graduate Student Symposium papers were carefully reviewed and selected from a total of 175 submissions.?The selected papers cover a wide range of topics, including machine learning, pattern recognition, natural language processing, knowledge representation, cognitive aspects of AI, ethics of AI, and other important aspects of AI research..
Pindex Conference proceedings 2020
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978-3-030-47357-0Springer Nature Switzerland AG 2020
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Michele Fioroni,Garry Tittertonory Core (RMC) as the cell state inside an LSTM cell using the standard multi-head self attention mechanism with variable length memory pointer and call it .. Two improvements are claimed: The area on which the RMC operates is expanded to create the new memory as more data is seen with each time ste
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Michele Fioroni,Garry Tittertonral network (CNN) training on stereo pair images with view reconstruction as a self-supervisory signal. In contrast to the previous work, we employ a stereo camera parameters estimation network to make our model robust to training data diversity. Another of our contributions is the introduction of s
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https://doi.org/10.1007/978-3-322-81629-0 cooperative environment. In this work, we present techniques for centralized training of Multi-Agent Deep Reinforcement Learning (MARL) using the model-free Deep Q-Network (DQN) as the baseline model and communication between agents. We present two novel, scalable and centralized MARL training tech
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