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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| arxiv 1707.04199 |
| Discriminative k-shot learning using probabilistic models | Matthias Bauer, Mateo Rojas-Carulla, Jakub Bart\lomiej Świątkowski | 2017 | arxiv 1706.00326 |
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| MLtuner: System Support for Automatic Machine Learning Tuning | Henggang Cui, Gregory R. Ganger, Phillip B. Gibbons | 2018 | arxiv 1803.07445 |
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| Knowledge Transfer for Melanoma Screening with Deep Learning | Afonso Menegola, Michel Fornaciali, Ramon Pires | 2017 | arxiv 1703.07479 |
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| Ordinal Pooling Networks: For Preserving Information over Shrinking Feature Maps | Ashwani Kumar | 2018 | arxiv 1804.02702 |
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| Energy Efficient Hadamard Neural Networks | T. Ceren Deveci, Serdar Cakir, A. Enis Cetin | 2018 | arxiv 1805.05421 |
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| Adversarial Examples in Remote Sensing | Wojciech Czaja, Neil Fendley, Michael Pekala | 2018 | arxiv 1805.10997 |
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| Factorized Adversarial Networks for Unsupervised Domain Adaptation | Jian Ren, Jianchao Yang, Ning Xu | 2018 | arxiv 1806.01376 |
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| Maximally Invariant Data Perturbation as Explanation | Satoshi Hara, Kouichi Ikeno, Tasuku Soma | 2018 | arxiv 1806.07004 |
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| Symbolic Execution for Deep Neural Networks | Divya Gopinath, Kaiyuan Wang, Mengshi Zhang | 2018 | arxiv 1807.10439 |
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| How do Convolutional Neural Networks Learn Design? | Shailza Jolly, Brian Kenji Iwana, Ryohei Kuroki | 2018 | arxiv 1808.08402 |
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