abstracts[] |
{'sha1': 'ec22d23b01a73902f9cc1dee128a75c150b8ad8e', 'content': 'This doctoral thesis covers some of my advances in electron microscopy with\ndeep learning. Highlights include a comprehensive review of deep learning in\nelectron microscopy; large new electron microscopy datasets for machine\nlearning, dataset search engines based on variational autoencoders, and\nautomatic data clustering by t-distributed stochastic neighbour embedding;\nadaptive learning rate clipping to stabilize learning; generative adversarial\nnetworks for compressed sensing with spiral, uniformly spaced and other fixed\nsparse scan paths; recurrent neural networks trained to piecewise adapt sparse\nscan paths to specimens by reinforcement learning; improving signal-to-noise;\nand conditional generative adversarial networks for exit wavefunction\nreconstruction from single transmission electron micrographs. This thesis adds\nto my publications by presenting their relationships, reflections, and holistic\nconclusions. This version of my thesis is typeset for online dissemination to\nimprove readability, whereas the thesis submitted to the University of Warwick\nin support of my application for the degree of Doctor of Philosophy in Physics\nis typeset for physical printing and binding.', 'mimetype': 'text/plain', 'lang': 'en'}
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contribs[] |
{'index': 0, 'creator_id': None, 'creator': None, 'raw_name': 'Jeffrey M. Ede', 'given_name': None, 'surname': None, 'role': 'author', 'raw_affiliation': None, 'extra': None}
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ext_ids |
{'doi': None, 'wikidata_qid': None, 'isbn13': None, 'pmid': None, 'pmcid': None, 'core': None, 'arxiv': '2101.01178v5', 'jstor': None, 'ark': None, 'mag': None, 'doaj': None, 'dblp': None, 'oai': None, 'hdl': None}
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issue |
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language |
en
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license_slug |
CC-BY-NC-SA
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[]
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release_date |
2021-03-11
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release_stage |
accepted
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release_type |
article
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release_year |
2021
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subtitle |
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title |
Advances in Electron Microscopy with Deep Learning
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version |
v5
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webcaptures |
[]
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work_id |
ls4pk435dfec5oweolqxbmikb4
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