ICML Workshop on TinyML: ML on a Test-time Budget for IoT, Mobiles, and Other Applications
Date: 10th August, 2017
Location: C4.7

We routinely encounter scenarios where at test-time we must predict on a budget. Feature costs in Internet, Healthcare, and Surveillance applications arise due to feature extraction time and feature/sensor acquisition costs. Data analytics applications in mobile devices are often performed on remote cloud services due to the limited device capabilities, which imposes memory/prediction time costs. Naturally, in these settings, one needs to carefully understand the trade-off between accuracy and prediction cost. Uncertainty in the observations, which is typical in such scenarios, further adds to complexity of the task and requires a careful understanding of both the uncertainty as well as accuracy-cost tradeoffs.

In this workshop, we aim to bring together researchers from various domains to discuss the key aspects of the above mentioned emerging and critical topic. The goal is to provide a platform where ML/statistics/optimization researchers can interact closely with domain experts who need to deploy ML models in resource-constrained settings (like an IoT device maker), and chart out the foundational problems in the area and key tools that can be used to solve them.

Invited Speakers (Tentative)
Forrest Iandola (DeepScale)
Bill March (Apple)
Shiv Naga Prasad (Amazon)
Sujith Ravi (Google)
Venkatesh Saligrama (Boston University)
Shaked Sammah (Mobileye)
Suchi Saria (JHU) 
Manik Varma (Microsoft Research)

Subpages (1): CFP