首页|Reports Outline Machine Learning Findings from Duke University (Machine Learning With Tree Tensor Networks, Cp Rank Constraints, and Tensor Dropout)
Reports Outline Machine Learning Findings from Duke University (Machine Learning With Tree Tensor Networks, Cp Rank Constraints, and Tensor Dropout)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ma chine Learning. According to news reportingoriginating from Durham, North Carol ina, by NewsRx correspondents, research stated, “Tensor networksdeveloped in th e context of condensed matter physics try to approximate order-N tensors with a reducednumber of degrees of freedom that is only polynomial in N and arranged a s a network of partially contractedsmaller tensors. As we have recently demonst rated in the context of quantum many-body physics,computation costs can be furt her substantially reduced by imposing constraints on the canonical polyadic(CP) rank of the tensors in such networks.”
DurhamNorth CarolinaUnited StatesN orth and Central AmericaCyborgsEmerging TechnologiesMachine LearningDuke University
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