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English Information

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Authors
# Name
1 Artur Correia(arturjlcorreia@gmail.com)
2 William Schwartz(william@dcc.ufmg.br)

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Reference
# Reference
1 Badia, A. P., Guoand, B. P. S. K. P. S. A. V. Z., and Blundell, C. (2020). Agent57: Outperforming the atari human benchmark. In International Conference on International Conference on Machine Learning (ICML).
2 He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Computer Vision and Pattern Recognition (CVPR).
3 Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., and Houlsby, N. (2020). Big transfer (bit): General visual representation learning. In European Conference on Computer Vision (ECCV).
4 Lacoste, A., Luccioni, A., Schmidt, V., and Dandres, T. (2019). Quantifying the carbon emissions of machine learning. In Neural Information Processing Systems (NeurIPS).
5 Li, Y., Yang, M., and Zhang, Z. (2019). A survey of multi-view representation learning. Transactions on Knowledge and Data Engineering, 31(10):1863–1883.
6 Luo, J.-H. and Wu, J. (2020). Neural network pruning with residual-connections and limited-data. In Conference on Computer Vision and Pattern Recognition (CVPR).
7 Sharma, A. and Jacobs, D. W. (2011). Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch. In Conference on Computer Vision and Pattern Recognition (CVPR).
8 Sindagi, V. and Patel, V. M. (2019). Multi-level bottom-top and top-bottom feature fusion for crowd counting. In International Conference on Computer Vision (ICCV).
9 Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Conference of the Association for Computational Linguistics.
10 Suau, X., Zappella, L., and Apostoloff, N. (2020). Filter distillation for network compression. In Winter Conference on Applications of Computer Vision (WACV).
11 Tan, M. and Le, Q. V. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning (ICML).
12 Yang, L., Han, Y., Chen, X., Song, S., Dai, J., and Huang, G. (2020). Resolution adaptive networks for efficient inference. In Conference on Computer Vision and Pattern Recognition (CVPR).
13 Zeng, X. and Li, G. (2014). Incremental partial least squares analysis of big streaming data. Pattern Recognition, 47:3726–3735.
14 Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. In Conference on Computer Vision and Pattern Recognition (CVPR).