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Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.
The results demonstrate that our approach obtain the better profit than other five approaches.
Our experiments demonstrate that our approach can effectively and efficiently predict the future neighborhood.
Results demonstrate that our approach is significantly efficient and scalable over existing data hiding techniques.
Also, the security analysis demonstrate that our approach can achieve desirable security properties of mobile wallet.
Experimental results demonstrate that our approach empirically surpasses strong feature based and neural network based methods.
Experiment results demonstrate that our approach outperforms other popular graph-based semi-supervised learning methods.
Experimental results demonstrate that our approach is intuitive, efficient, and robust in visual subspace clustering.
Experimental results demonstrate that our approach can identify malicious MSN nodes faster than other similar approaches.
A series of experiments demonstrate that our approach is effective and able to generate practical results.
Experiments demonstrate that our approach is superior to an agent using a single learner.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com