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pro vyhledávání: '"Han , Jiyeon"'
Many contextualized word representations are now learned by intricate neural network models, such as masked neural language models (MNLMs) which are made up of huge neural network structures and trained to restore the masked text. Such representation
Externí odkaz:
http://arxiv.org/abs/2209.00599
Evaluation metrics in image synthesis play a key role to measure performances of generative models. However, most metrics mainly focus on image fidelity. Existing diversity metrics are derived by comparing distributions, and thus they cannot quantify
Externí odkaz:
http://arxiv.org/abs/2206.08549
Despite significant improvements on the image generation performance of Generative Adversarial Networks (GANs), generations with low visual fidelity still have been observed. As widely used metrics for GANs focus more on the overall performance of th
Externí odkaz:
http://arxiv.org/abs/2112.08814
Publikováno v:
In Computers in Biology and Medicine May 2024 174
Generative Adversarial Networks (GANs) have shown satisfactory performance in synthetic image generation by devising complex network structure and adversarial training scheme. Even though GANs are able to synthesize realistic images, there exists a n
Externí odkaz:
http://arxiv.org/abs/2104.06118
Currently, contextualized word representations are learned by intricate neural network models, such as masked neural language models (MNLMs). The new representations significantly enhanced the performance in automated question answering by reading pa
Externí odkaz:
http://arxiv.org/abs/1911.03024
In the analysis of sequential data, the detection of abrupt changes is important in predicting future changes. In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by
Externí odkaz:
http://arxiv.org/abs/1905.13168
Publikováno v:
In Coordination Chemistry Reviews 1 March 2023 478
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