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FAST LOW-MEMORY METHODS FOR BAYESIAN INFERENCE, GIBBS SAMPLING AND DEEP LEARNING
专利权人:
MICROSOFT TECHNOLOGY LICENSING, LLC
发明人:
WIEBE, Nathan,KAPOOR, Ashish,SVORE, Krysta,GRANADE, Christopher
申请号:
WO2016US32942
公开号:
WO2016196005(A1)
申请日:
2016.05.18
申请国别(地区):
世界知识产权组织国际局
年份:
2016
代理人:
摘要:
Methods of training Boltzmann machines include rejection sampling to approximate a Gibbs distribution associated with layers of the Boltzmann machine. Accepted sample values obtained using a set of training vectors and a set of model values associate with a model distribution are processed to obtain gradients of an objective function so that the Boltzmann machine specification can be updated. In other examples, a Gibbs distribution is estimated or a quantum circuit is specified so at to produce eigenphases of a unitary.
来源网站:
中国工程科技知识中心
来源网址:
http://www.ckcest.cn/home/

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