6 research outputs found
MultiIoT: Towards Large-scale Multisensory Learning for the Internet of Things
The Internet of Things (IoT), the network integrating billions of smart
physical devices embedded with sensors, software, and communication
technologies for the purpose of connecting and exchanging data with other
devices and systems, is a critical and rapidly expanding component of our
modern world. The IoT ecosystem provides a rich source of real-world modalities
such as motion, thermal, geolocation, imaging, depth, sensors, video, and audio
for prediction tasks involving the pose, gaze, activities, and gestures of
humans as well as the touch, contact, pose, 3D of physical objects. Machine
learning presents a rich opportunity to automatically process IoT data at
scale, enabling efficient inference for impact in understanding human
wellbeing, controlling physical devices, and interconnecting smart cities. To
develop machine learning technologies for IoT, this paper proposes MultiIoT,
the most expansive IoT benchmark to date, encompassing over 1.15 million
samples from 12 modalities and 8 tasks. MultiIoT introduces unique challenges
involving (1) learning from many sensory modalities, (2) fine-grained
interactions across long temporal ranges, and (3) extreme heterogeneity due to
unique structure and noise topologies in real-world sensors. We also release a
set of strong modeling baselines, spanning modality and task-specific methods
to multisensory and multitask models to encourage future research in
multisensory representation learning for IoT
Learning Structured, Robust, and Multimodal Models
Non UBCUnreviewedAuthor affiliation: University of TorontoFacult
Annealing Between Distributions by Averaging Moments
Many powerful Monte Carlo techniques for estimating partition functions, such as annealed importance sampling (AIS), are based on sampling from a sequence of intermediate distributions which interpolate between a tractable initial distribution and the intractable target distribution. The near-universal practice is to use geometric averages of the initial and target distributions, but alternative paths can perform substantially better. We present a novel sequence of intermediate distributions for exponential families defined by averaging the moments of the initial and target distributions. We analyze the asymptotic performance of both the geometric and moment averages paths and derive an asymptotically optimal piecewise linear schedule. AIS with moment averaging performs well empirically at estimating partition functions of restricted Boltzmann machines (RBMs), which form the building blocks of many deep learning models, including Deep Belief Networks and Deep Boltzmann Machines. Joint work with Roger Grosse and Chris Maddison.
References:
Annealing between Distributions by Averaging Moments. Roger Grosse, Chris Maddison, and Ruslan Salakhutdinov. In Neural Information Processing Systems (NIPS 27) www.cs.toronto.edu/~rsalakhu/papers/nips2013_moment.pdfNon UBCUnreviewedAuthor affiliation: University of TorontoFacult