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 substanti...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 methods compared to the causal,hypothesis driven nature of modern science.We argue that the broad adoption of Bayesian methods incorporating prior knowledge,development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models,and ultimately adoption of causal models,offers a path forward for fundamental and applied research.展开更多
基金The work was supported by the U.S.Department of Energy,Office of Science,Materials Sciences and Engineering Division(S.V.K.,L.V.,R.K.V.).
文摘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 methods compared to the causal,hypothesis driven nature of modern science.We argue that the broad adoption of Bayesian methods incorporating prior knowledge,development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models,and ultimately adoption of causal models,offers a path forward for fundamental and applied research.
