首页|Studies from Washington University St. Louis Have Provided New Information about Machine Learning (Access-redundancy Tradeoffs In Quantized Linear Computations)
Studies from Washington University St. Louis Have Provided New Information about Machine Learning (Access-redundancy Tradeoffs In Quantized Linear Computations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news originatingfrom St. Louis, Missouri, by New sRx correspondents, research stated, “Linear real-valued computationsover distr ibuted datasets are common in many applications, most notably as part of machine learninginference. In particular, linear computations that are quantized, i.e. , where the coefficients are restrictedto a predetermined set of values (such a s +/- 1), have gained increasing interest lately due to their rolein efficient, robust, or private machine learning models.”
St. LouisMissouriUnited StatesNort h and Central AmericaCyborgsEmerging TechnologiesMachine LearningWashing ton University St. Louis
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