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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| 2024 |
| arxiv 2410.17193 |
| The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods | Louis Thiry, Michael Arbel, Eugene Belilovsky | 2021 | arxiv 2101.07528 |
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| Learned Threshold Pruning | Kambiz Azarian, Yash Bhalgat, Jinwon Lee | 2020 | arxiv 2003.00075 |
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| AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling | Dilin Wang, Meng Li, Chengyue Gong | 2020 | arxiv 2011.09011 |
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| "BNN - BN = ?": Training Binary Neural Networks without Batch Normalization | Tianlong Chen, Zhenyu Zhang, Xu Ouyang | 2021 | arxiv 2104.08215 |
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| In-Hindsight Quantization Range Estimation for Quantized Training | Marios Fournarakis, Markus Nagel | 2021 | arxiv 2105.04246 |
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| Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded Platforms | Wei Lou, Lei Xun, Amin Sabet | 2021 | arxiv 2105.03596 |
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| BCNet: Searching for Network Width with Bilaterally Coupled Network | Xiu Su, Shan You, Fei Wang | 2021 | arxiv 2105.10533 |
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| Accelerating Neural Architecture Search via Proxy Data | Byunggook Na, Jisoo Mok, Hyeokjun Choe | 2021 | arxiv 2106.04784 |
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| Merged-GHCIDR: Geometrical Approach to Reduce Image Data | Devvrat Joshi, Janvi Thakkar, Siddharth Soni | 2022 | arxiv 2209.02609 |
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| Enhance the Visual Representation via Discrete Adversarial Training | Xiaofeng Mao, Yuefeng Chen, Ranjie Duan | 2022 | arxiv 2209.07735 |
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