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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| PyTorch-Hebbian: facilitating local learning in a deep learning framework |
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| Jules Talloen, Joni Dambre, Alexander Vandesompele |
| 2021 |
| arxiv 2102.00428 |
| Multi-Instance Learning by Utilizing Structural Relationship among Instances | Yangling Ma, Zhouwang Yang | 2021 | arxiv 2102.01889 |
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| Tent: Fully Test-time Adaptation by Entropy Minimization | Dequan Wang, Evan Shelhamer, Shaoteng Liu | 2020 | arxiv 2006.10726 |
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| Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations | Alex Wong, Mukund Mundhra, Stefano Soatto | 2020 | arxiv 2009.10142 |
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| Reinforced Attention for Few-Shot Learning and Beyond | Jie Hong, Pengfei Fang, Weihao Li | 2021 | arxiv 2104.04192 |
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| Submodular Mutual Information for Targeted Data Subset Selection | Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan | 2021 | arxiv 2105.00043 |
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| Positive-Congruent Training: Towards Regression-Free Model Updates | Sijie Yan, Yuanjun Xiong, Kaustav Kundu | 2020 | arxiv 2011.09161 |
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| Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding | Pengchuan Zhang, Xiyang Dai, Jianwei Yang | 2021 | arxiv 2103.15358 |
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| AutoSampling: Search for Effective Data Sampling Schedules | Ming Sun, Haoxuan Dou, Baopu Li | 2021 | arxiv 2105.13695 |
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| MLP-Mixer: An all-MLP Architecture for Vision | Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov | 2021 | arxiv 2105.01601 |
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