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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Mehrdad Asadi, Komi Sodoké, Ian J. Gerard |
| 2025 |
| arxiv 2502.03591 |
| LiD-FL: Towards List-Decodable Federated Learning | Hong Liu, Liren Shan, Han Bao | 2024 | arxiv 2408.04963 |
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| A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data | Weicheng Cai, Ming Li | 2020 | arxiv 2012.00486 |
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| Difficulty in estimating visual information from randomly sampled images | Masaki Kitayama, Hitoshi Kiya | 2020 | arxiv 2012.08751 |
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| "Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us? | Bin Liu | 2021 | arxiv 2103.15294 |
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| Object Segmentation Without Labels with Large-Scale Generative Models | Andrey Voynov, Stanislav Morozov, Artem Babenko | 2020 | arxiv 2006.04988 |
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| MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data | Arpit Bansal, Micah Goldblum, Valeriia Cherepanova | 2021 | arxiv 2106.09643 |
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| End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network | Avi Gazneli, Gadi Zimerman, Tal Ridnik | 2022 | arxiv 2204.11479 |
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| The Computational Limits of Deep Learning | Neil C. Thompson, Kristjan Greenewald, Keeheon Lee | 2020 | arxiv 2007.05558 |
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| Maximal Independent Sets for Pooling in Graph Neural Networks | Stevan Stanovic, Benoit Gaüzère, Luc Brun | 2023 | arxiv 2307.13011 |
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