A long-standing vision in computer science has been to evolve computing
devices into proactive assistants that enhance our productivity, health and
wellness, and many other facets of our lives. User digitization is crucial in
achieving this vision as it allows computers to intimately understand their
users, capturing activity, pose, routine, and behavior. Today's consumer
devices - like smartphones and smartwatches provide a glimpse of this
potential, offering coarse digital representations of users with metrics such
as step count, heart rate, and a handful of human activities like running and
biking. Even these very low-dimensional representations are already bringing
value to millions of people's lives, but there is significant potential for
improvement. On the other end, professional, high-fidelity comprehensive user
digitization systems exist. For example, motion capture suits and multi-camera
rigs that digitize our full body and appearance, and scanning machines such as
MRI capture our detailed anatomy. However, these carry significant user
practicality burdens, such as financial, privacy, ergonomic, aesthetic, and
instrumentation considerations, that preclude consumer use. In general, the
higher the fidelity of capture, the lower the user's practicality. Most
conventional approaches strike a balance between user practicality and
digitization fidelity.
My research aims to break this trend, developing sensing systems that
increase user digitization fidelity to create new and powerful computing
experiences while retaining or even improving user practicality and
accessibility, allowing such technologies to have a societal impact. Armed with
such knowledge, our future devices could offer longitudinal health tracking,
more productive work environments, full body avatars in extended reality, and
embodied telepresence experiences, to name just a few domains.

通过开发传感系统，提高用户数字化的准确性，同时保持甚至提高用户实用性和可访问性，实现新的、强大的计算体验，从而使这些技术产生社会影响。

实用且丰富的用户数字化

Practical and Rich User Digitization

The huge size of deep networks hinders their use in small computing devices.
In this paper, we consider compressing the network by weight quantization. We
extend a recently proposed loss-aware weight binarization scheme to
ternarization, with possibly different scaling parameters for the positive and
negative weights, and m-bit (where m > 2) quantization. Experiments on
feedforward and recurrent neural networks show that the proposed scheme
outperforms state-of-the-art weight quantization algorithms, and is as accurate
(or even more accurate) than the full-precision network.

本文考虑使用权重量化压缩深度网络，将最近提出的有损权重二值化方案推广到三值化，并在前馈和循环神经网络上进行实验，发现该方案优于现有的权重量化算法，精度与全精度网络相当或更高。