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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Yash Goyal, Ziyan Wu, Jan Ernst |
| 2019 |
| arxiv 1904.07451 |
| Training Deep Neural Networks Using Posit Number System | Jinming Lu, Siyuan Lu, Zhisheng Wang | 2019 | arxiv 1909.03831 |
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| Robust Classification using Robust Feature Augmentation | Kevin Eykholt, Swati Gupta, Atul Prakash | 2019 | arxiv 1905.10904 |
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| Generative Image Translation for Data Augmentation in Colorectal Histopathology Images | Jerry Wei, Arief Suriawinata, Louis Vaickus | 2019 | arxiv 1910.05827 |
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| Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift | Xudong Sun, Alexej Gossmann, Yu Wang | 2019 | arxiv 1906.02972 |
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| Addressing Failure Prediction by Learning Model Confidence | Charles Corbière, Nicolas Thome, Avner Bar-Hen | 2019 | arxiv 1910.04851 |
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| MUTE: Data-Similarity Driven Multi-hot Target Encoding for Neural Network Design | Mayoore S. Jaiswal, Bumsoo Kang, Jinho Lee | 2019 | arxiv 1910.07042 |
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| Improving Machine Reading Comprehension via Adversarial Training | Ziqing Yang, Yiming Cui, Wanxiang Che | 2019 | arxiv 1911.03614 |
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| Hierarchically Structured Meta-learning | Huaxiu Yao, Ying Wei, Junzhou Huang | 2019 | arxiv 1905.05301 |
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| LPRNet: Lightweight Deep Network by Low-rank Pointwise Residual Convolution | Bin Sun, Jun Li, Ming Shao | 2019 | arxiv 1910.11853 |
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