CM Seminar: Can machine learning outperform a condensed-matter physicist?
Event Start:
2019-09-05T14:00:00
Event End:
2019-09-05T15:00:00
Event Information:
Abstract: Machine learning models are usually trained by a large number of observations (big data) to make predictions through the evaluation of complex mathematical objects. However, in many applications in science, particularly in quantum condensed-matter physics, obtaining observables is expensive so information is limited. In the present work, we consider the limit of ‘small data’. Usually, ‘big data’ are for machines and ‘small data’ are for humans, i.e.
Event Location:
BRIMACOMBE 311
Speaker:
Roman Krems, UBC Department of Chemistry
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Event Start:
2019-09-05T14:00:00
Event End:
2019-09-05T15:00:00
CM Seminar: Can machine learning outperform a condensed-matter physicist?
Event Information:
Abstract: Machine learning models are usually trained by a large number of observations (big data) to make predictions through the evaluation of complex mathematical objects. However, in many applications in science, particularly in quantum condensed-matter physics, obtaining observables is expensive so information is limited. In the present work, we consider the limit of ‘small data’. Usually, ‘big data’ are for machines and ‘small data’ are for humans, i.e.
Event Location:
BRIMACOMBE 311