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In NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00020"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"e_1_3_2_1_51_1","volume-title":"And the Bit Goes Down: Revisiting the Quantization of Neural Networks. In ICLR 2020-Eighth International Conference on Learning Representations. 1--11","author":"Stock Pierre","year":"2020","unstructured":"Pierre Stock , Armand Joulin , R\u00e9mi Gribonval , Benjamin Graham , and Herv\u00e9 J\u00e9gou . 2020 . And the Bit Goes Down: Revisiting the Quantization of Neural Networks. In ICLR 2020-Eighth International Conference on Learning Representations. 1--11 . Pierre Stock, Armand Joulin, R\u00e9mi Gribonval, Benjamin Graham, and Herv\u00e9 J\u00e9gou. 2020. And the Bit Goes Down: Revisiting the Quantization of Neural Networks. In ICLR 2020-Eighth International Conference on Learning Representations. 1--11."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/FPL50879.2020.00055"},{"key":"e_1_3_2_1_53_1","volume-title":"Bowman","author":"Wang Alex","year":"2018","unstructured":"Alex Wang , Amanpreet Singh , Julian Michael , Felix Hill , Omer Levy , and Samuel R . Bowman . 2018 . GLUE : A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. CoRR abs\/1804.07461 (2018). arXiv:1804.07461 http:\/\/arxiv.org\/abs\/1804.07461 Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2018. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. 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