Real-time Learning and Inference on Emerging Mobile Systems Vikas Sindhwani Google May 17-19, 2016 2016 Summer School on Signal Processing and Machine Learning for Big Data

Abstract: We are motivated by the challenge of enabling real-time "always-on" machine learning applications on emerging mobile platforms such as next-generation smartphones, wearable computers and consumer robotics systems. On-device models in such settings need to be highly compact, and need to support fast, low-power inference on specialized hardware. I will consider the problem of building small-footprint non- linear models based on kernel methods and deep learning techniques, for on-device deployments. Towards this end, I will give an overview of various techniques, and introduce new notions of parsimony rooted in the theory of structured matrices. Such structured matrices can be used to recycle Gaussian random vectors in order to build randomized feature maps in sub-linear time for approximating various kernel functions. In the deep learning context, low-displacement structured parameter matrices admit fast function and gradient evaluation. I will discuss how such compact nonlinear transforms span a rich range of parameter sharing configurations whose statistical modeling capacity can be explicitly tuned along a continuum from structured to unstructured. I will present empirical results on mobile speech recognition problems, and image classification tasks. I will also briefly present some basics of TensorFlow: a open-source library for numerical computations on data flow graphs. Tensorflow enables large-scale distributed training of complex machine learning models, and their rapid deployment on mobile devices.

Bio: Vikas Sindhwani is Research Scientist in the Google Brain team in New York City. His interests are broadly in core mathematical foundations of statistical learning, and in end-to-end design aspects of building large-scale, robust machine intelligence systems. He received the best paper award at Uncertainty in Artificial Intelligence (UAI) 2013, the IBM Pat Goldberg Memorial Award in 2014, and was co-winner of the Knowledge Discovery and Data Mining (KDD) Cup in 2009. He previously led the Machine Learning group at IBM Research, NY, and has a PhD in CS from the University of Chicago. His publications are available at: http://vikas.sindhwani.org/.