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Compared to the random predictor, our method achieved much better performance.
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This method achieves much more attractive space time tradeoffs.
Extensive experiments validate that the proposed method achieves much better results.
Figure 4 shows that our method achieves much better performance than the method in [8] on the same Lena image.
Comprehensive experiments show that this feature fusion based method achieves much better performances compared to traditional methods.
Comparing with the DB method without particular nulling directions, the PODB method achieves much higher chances of transmitting less power towards directions of LU.
The results show that the proposed method achieves much better reconstruction of skeletal and mesh animation than the other methods under analysis.
As the overhead is relatively low, the proposed method achieves much higher reward than all the other three methods, because it takes into account the effects of both the overhead and the moving speed on the reward function.
From Figure 7 and Table 4, we can see that the proposed method achieves much lower EER than the traditional score-level fusion methods in each type of finger.
Finally, several representative simulations have been given to show that the proposed method achieves much better MSE performance than NSS with respect to different signal sparsities, especially in the case of low SNR regime.
The proposed method achieves much more improvement on the ESP-Game dataset by comparing with a bit improvement on the Corel-5K dataset, demonstrating its effectiveness of modeling the large-scale dataset.
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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