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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Steven Basart |
| 2021 |
| arxiv 2112.15188 |
| Biologically-plausible learning algorithms can scale to large datasets | Will Xiao, Honglin Chen, Qianli Liao | 2018 | arxiv 1811.03567 |
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| MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features | Shakti N. Wadekar, Abhishek Chaurasia | 2022 | arxiv 2209.15159 |
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| Precision at Scale: Domain-Specific Datasets On-Demand | Jesús M Rodríguez-de-Vera, Imanol G Estepa, Ignacio Sarasúa | 2024 | arxiv 2407.03463 |
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| EMP-SSL: Towards Self-Supervised Learning in One Training Epoch | Shengbang Tong, Yubei Chen, Yi Ma | 2023 | arxiv 2304.03977 |
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| Leveraging the Third Dimension in Contrastive Learning | Sumukh Aithal, Anirudh Goyal, Alex Lamb | 2023 | arxiv 2301.11790 |
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| SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers | Nick Nikzad, Yi Liao, Yongsheng Gao | 2024 | arxiv 2409.19850 |
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| Comparative Analysis of Lightweight CNNs for Resource-Constrained Devices: Predictive Performance, Efficiency Trade-offs, and Initialization Effects | Tasnim Shahriar | 2025 | arxiv 2505.03303 |
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| Deep Deterministic Uncertainty: A Simple Baseline | Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort | 2021 | arxiv 2102.11582 |
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| Multi-task pre-training of deep neural networks for digital pathology | Romain Mormont, Pierre Geurts, Raphaël Marée | 2020 | arxiv 2005.02561 |
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