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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| 2021 |
| arxiv 2108.05055 |
| Incremental Learning with Differentiable Architecture and Forgetting Search | James Seale Smith, Zachary Seymour, Han-Pang Chiu | 2022 | arxiv 2205.09875 |
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| Asynchronous Hierarchical Federated Learning | Xing Wang, Yijun Wang | 2022 | arxiv 2206.00054 |
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| Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks | Runpei Dong, Zhanhong Tan, Mengdi Wu | 2021 | arxiv 2112.15139 |
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| Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention | Kai Liu, Tianyi Wu, Cong Liu | 2022 | arxiv 2203.03937 |
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| Set Based Stochastic Subsampling | Bruno Andreis, Seanie Lee, A. Tuan Nguyen | 2020 | arxiv 2006.14222 |
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| Exploring the Open World Using Incremental Extreme Value Machines | Tobias Koch, Felix Liebezeit, Christian Riess | 2022 | arxiv 2205.14892 |
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| Tackling covariate shift with node-based Bayesian neural networks | Trung Trinh, Markus Heinonen, Luigi Acerbi | 2022 | arxiv 2206.02435 |
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| OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework | Peng Wang, An Yang, Rui Men | 2022 | arxiv 2202.03052 |
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| Training Neural Networks using SAT solvers | Subham S. Sahoo | 2022 | arxiv 2206.04833 |
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| Deep Learning Development Environment in Virtual Reality | Kevin C. VanHorn, Meyer Zinn, Murat Can Cobanoglu | 2019 | arxiv 1906.05925 |
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