September 09, 2019

Unsupervised Domain Adaptation for Design of Robust Sensory Systems

  • Berthouze N.
  • Isopoussu A.
  • Kawsar F.
  • Lane N.
  • Mathur A.

Despite significant advances in the performance of sensory inference models, their poor robustness to changing environmental conditions and hardware remains a major hurdle for widespread adoption. In this paper, we introduce the concept of unsupervised domain adaptation which is a technique to adapt sensory inference models to new domains only using unlabeled data from the target domain. We present two case-studies to motivate the problem and highlight some of our recent work in this space. Finally, we discuss the core challenges in this space that can trigger further ubicomp research on this topic and also lead to interesting discussions at the workshop.

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© 1963-2012 IEEE. This article presents a packaged SiGe BiCMOS reflectometer for 0.01-26-GHz two-port vector network analyzers (VNAs). The reflectometer chip is composed of a resistive bridge coupler and two wideband heterodyne receivers for coherent magnitude and phase detection. In addition, a high-linearity receiver channel is designed to accommodate 20 ...

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The use of topology to protect quantum information is well-known to the condensed-matter community and, indeed, topological quantum computing is a bursting field of research and one of the competing avenues to demonstrate that quantum computers can complete certain problems that classical computers cannot. In photonics, however, we are only ...

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This paper takes a fresh look at the debate over the relationship between digital technology and productivity. The argument of economic historian Robert J. Gordon is that digital technology will not lead to increases in productivity such as we saw in the last century, based on his analysis of the ...