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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Yongchan Kwon, Wonyoung Kim, Joong-Ho Won |
| 2020 |
| arxiv 2006.03333 |
| CacheNet: A Model Caching Framework for Deep Learning Inference on the Edge | Yihao Fang, Shervin Manzuri Shalmani, Rong Zheng | 2020 | arxiv 2007.01793 |
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| Learning to Learn Parameterized Classification Networks for Scalable Input Images | Duo Li, Anbang Yao, Qifeng Chen | 2020 | arxiv 2007.06181 |
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| GOLD-NAS: Gradual, One-Level, Differentiable | Kaifeng Bi, Lingxi Xie, Xin Chen | 2020 | arxiv 2007.03331 |
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| Low-Rank Subspace Override for Unsupervised Domain Adaptation | Christoph Raab, Frank-Michael Schleif | 2019 | arxiv 1907.01343 |
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| Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks | Rémi Bernhard, Pierre-Alain Moellic, Jean-Max Dutertre | 2019 | arxiv 1909.12741 |
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| Confidence Regularized Self-Training | Yang Zou, Zhiding Yu, Xiaofeng Liu | 2019 | arxiv 1908.09822 |
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| Geodesics of learned representations | Olivier J. Hénaff, Eero P. Simoncelli | 2015 | arxiv 1511.06394 |
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| Rethinking Normalization and Elimination Singularity in Neural Networks | Siyuan Qiao, Huiyu Wang, Chenxi Liu | 2019 | arxiv 1911.09738 |
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| Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network | Anh-Huy Phan, Konstantin Sobolev, Konstantin Sozykin | 2020 | arxiv 2008.05441 |
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| Improving Self-Organizing Maps with Unsupervised Feature Extraction | Lyes Khacef, Laurent Rodriguez, Benoit Miramond | 2020 | arxiv 2009.02174 |
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