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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2022; 31st International C Elias Pimenidis,Plamen Angelov,Mehmet Aydin Conference p

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樓主: 母牛膽小鬼
61#
發(fā)表于 2025-4-1 05:01:39 | 只看該作者
,Word-by-Word Generation of?Visual Dialog Using Reinforcement Learning, and processing visual content. While previous research focused on arranging questions to form dialog, we tackle the more challenging task of arranging questions from words, and dialog from questions. We develop our model in a simple “Guess which?” game scenario where the agent needs to predict an i
62#
發(fā)表于 2025-4-1 06:31:26 | 只看該作者
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發(fā)表于 2025-4-1 10:25:35 | 只看該作者
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發(fā)表于 2025-4-1 17:08:05 | 只看該作者
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發(fā)表于 2025-4-1 20:55:07 | 只看該作者
,An Unsupervised Sentence Embedding Method by?Maximizing the?Mutual Information of?Augmented Text Reanner. However, unsupervised methods pale by comparison to supervised ones on many tasks. Recently, some unsupervised methods propose to learn sentence representations by maximizing the mutual information between text representations of different levels, such as global MI maximization: global and gl
66#
發(fā)表于 2025-4-1 23:11:25 | 只看該作者
Analysis of COVID-19 5G Conspiracy Theory Tweets Using SentenceBERT Embedding,conspiracy theories in different subjects, with COVID-19 conspiracies among them. In this research, we collected a dataset of 331,448 tweets related to the COVID-19 5G conspiracy theory. We present a workflow to collect, classify, and analyze conspiracy related tweets as supporting or opposing the c
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