| Abstract Scope |
Deep learning schemes have already impacted areas such as cognitive game theory (e.g., computer chess and the game of Go), pattern (e.g., facial or fingerprint) recognition, event forecasting, and bioinformatics. They are beginning to make major inroads within physics, chemistry and materials sciences and hold considerable promise for accelerating the discovery of new theories and materials. In this talk, I will introduce deep convolutional neural networks, and how they can be applied to decoding time-domain compressed TEM video streams to improve time resolution by orders of magnitude and how it can solve the missing-wedge problem in tomographic imaging through information inpainting in a high-dimensional manifold. |