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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Regularizing Neural Networks by Stochastically Training Layer Ensembles |
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| Alex Labach, Shahrokh Valaee |
| 2019 |
| arxiv 1911.09669 |
| Maximal function pooling with applications | Wojciech Czaja, Weilin Li, Yiran Li | 2021 | arxiv 2103.01292 |
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| Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes | Yusuke Hirota, Jerone T. A. Andrews, Dora Zhao | 2024 | arxiv 2407.03623 |
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| AFN: Adaptive Fusion Normalization via an Encoder-Decoder Framework | Zikai Zhou, Shuo Zhang, Ziruo Wang | 2023 | arxiv 2308.03321 |
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| Classification for everyone : Building geography agnostic models for fairer recognition | Akshat Jindal, Shreya Singh, Soham Gadgil | 2023 | arxiv 2312.02957 |
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| On the rate of convergence of image classifiers based on convolutional neural networks | M. Kohler, A. Krzyzak, B. Walter | 2020 | arxiv 2003.01526 |
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| Training Stacked Denoising Autoencoders for Representation Learning | Jason Liang, Keith Kelly | 2021 | arxiv 2102.08012 |
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| Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling | Benjamin Walter | 2021 | arxiv 2106.05233 |
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| Fine-Grained Few Shot Learning with Foreground Object Transformation | Chaofei Wang, Shiji Song, Qisen Yang | 2021 | arxiv 2109.05719 |
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| When does the student surpass the teacher? Federated Semi-supervised Learning with Teacher-Student EMA | Jessica Zhao, Sayan Ghosh, Akash Bharadwaj | 2023 | arxiv 2301.10114 |
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