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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Adaptive Learning Rate and Momentum for Training Deep Neural Networks | Zhiyong Hao, Yixuan Jiang, Huihua Yu | 2021 | arxiv 2106.11548 |
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| Knowledge accumulating: The general pattern of learning | Zhuoran Xu, Hao Liu | 2021 | arxiv 2108.03988 |
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| Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings | Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko | 2020 | arxiv 2010.08666 |
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| Better Aggregation in Test-Time Augmentation | Divya Shanmugam, Davis Blalock, Guha Balakrishnan | 2020 | arxiv 2011.11156 |
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| Deep Learning Based Brain Tumor Segmentation: A Survey | Zhihua Liu, Lei Tong, Zheheng Jiang | 2020 | arxiv 2007.09479 |
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| Human Imperceptible Attacks and Applications to Improve Fairness | Xinru Hua, Huanzhong Xu, Jose Blanchet | 2021 | arxiv 2111.15603 |
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| High-Order Progressive Trajectory Matching for Medical Image Dataset Distillation | Le Dong, Jinghao Bian, Jingyang Hou | 2025 | arxiv 2509.24177 |
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| QuadEnhancer: Leveraging Quadratic Transformations to Enhance Deep Neural Networks | Qian Chen, Linxin Yang, Akang Wang | 2025 | arxiv 2510.03276 |
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| Uncertainty evaluation of segmentation models for Earth observation | Melanie Rey, Andriy Mnih, Maxim Neumann | 2025 | arxiv 2510.19586 |
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| Fusion framework and multimodality for the Laplacian approximation of Bayesian neural networks | Magnus Malmström, Isaac Skog, Daniel Axehill | 2023 | arxiv 2310.08315 |
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