{"id":687630,"date":"2020-08-26T12:22:07","date_gmt":"2020-08-26T19:22:07","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=687630"},"modified":"2020-08-26T12:22:07","modified_gmt":"2020-08-26T19:22:07","slug":"learning-nonlinear-dynamic-models","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-nonlinear-dynamic-models\/","title":{"rendered":"Learning Nonlinear Dynamic Models"},"content":{"rendered":"

We present a novel approach for learning nonlinear dynamic models, which leads to a new set of tools capable of solving problems that are otherwise difficult. We provide theory showing this new approach is consistent for models with long range structure, and apply the approach to motion capture and high-dimensional video data, yielding results superior to standard alternatives.<\/p>\n","protected":false},"excerpt":{"rendered":"

We present a novel approach for learning nonlinear dynamic models, which leads to a new set of tools capable of solving problems that are otherwise difficult. We provide theory showing this new approach is consistent for models with long range structure, and apply the approach to motion capture and high-dimensional video data, yielding results superior 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