Towards More Generalizable Neural Networks via Modularity
Artificial neural networks have become highly effective at performing specific, challenging tasks by leveraging a large amount of training data. However, they are unable to generalize to diverse, unseen domains without requiring significant retraining. This thesis quantifies the generalization diffi...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/144929 |