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We present new deep learning architectures that preserve the multi-dimensional information in data end-to-end.I will describe the underlying lightweight hierarchical parameter server architecture that results in high efficiency in distributed settings.Pushing the current boundaries of deep learning requires using multiple dimensions and modalities.We show that tensor contractions and regression layers are an effective replacement for fully connected layers in deep learning architectures.They result in significant space savings with negligible performance degradation.He also helped build the infrastructure to scale Open AI’s distributed ML systems to thousands of machines.
Machine Learning Systems at Scale: Open AI is a non-profit research company, discovering and enacting the path to safe artificial general intelligence.
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She was a postdoctoral researcher at MIT from 2009 to 2010, an assistant professor at U. Irvine between 20, and a visiting researcher at Microsoft Research New England in 20.