Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality
Abstract Deep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-01-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-020-00487-0 |