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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| InterpNET: Neural Introspection for Interpretable Deep Learning |
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| Shane Barratt |
| 2017 |
| arxiv 1710.09511 |
| Weakly Supervised Semantic Segmentation using Web-Crawled Videos | Seunghoon Hong, Donghun Yeo, Suha Kwak | 2017 | arxiv 1701.00352 |
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| FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks | Suo Qiu, Xiangmin Xu, Bolun Cai | 2017 | arxiv 1706.08098 |
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| Wide Compression: Tensor Ring Nets | Wenqi Wang, Yifan Sun, Brian Eriksson | 2018 | arxiv 1802.09052 |
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| Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution | Lanlan Liu, Jia Deng | 2017 | arxiv 1701.00299 |
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| Fast Parametric Learning with Activation Memorization | Jack W Rae, Chris Dyer, Peter Dayan | 2018 | arxiv 1803.10049 |
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| CRAM: Clued Recurrent Attention Model | Minki Chung, Sungzoon Cho | 2018 | arxiv 1804.10844 |
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| Who Let The Dogs Out? Modeling Dog Behavior From Visual Data | Kiana Ehsani, Hessam Bagherinezhad, Joseph Redmon | 2018 | arxiv 1803.10827 |
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| A Novel Framework for Recurrent Neural Networks with Enhancing Information Processing and Transmission between Units | Xi Chen, Zhihong Deng, Gehui Shen | 2018 | arxiv 1806.00628 |
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| Learning Longer-term Dependencies in RNNs with Auxiliary Losses | Trieu H. Trinh, Andrew M. Dai, Minh-Thang Luong | 2018 | arxiv 1803.00144 |
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