Training-free neural active learning with initialization robustness guarantees
Neural active learning techniques so far have focused on enhancing the predic- tive capabilities of the networks. However, safety-critical applications necessi- tate not only good predictive performance but also robustness to randomness in the model-fitting process. To address this, we present th...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166498 |