Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors

no code implementations • 23 Feb 2022 • Cameron Haigh, Zichen Zhang, Negar Hassanpour, Khurram Javed, Yingying Fu, Shayan Shahramian, Shawn Zhang, Jun Luo

In light of the need to tweak the target specifications throughout the circuit design cycle, we also develop a variant in which the agent can learn to quickly adapt to draw new inductors for moderately different target specifications.

Reinforcement Learning (RL)

OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery

no code implementations • 14 Nov 2020 • Hao Sheng, Jeremy Irvin, Sasankh Munukutla, Shawn Zhang, Christopher Cross, Kyle Story, Rose Rustowicz, Cooper Elsworth, Zutao Yang, Mark Omara, Ritesh Gautam, Robert B. Jackson, Andrew Y. Ng

In this work, we develop deep learning algorithms that leverage freely available high-resolution aerial imagery to automatically detect oil and gas infrastructure, one of the largest contributors to global methane emissions.

Attribute

Report on the First Knowledge Graph Reasoning Challenge 2018 -- Toward the eXplainable AI System

no code implementations • 22 Aug 2019 • Takahiro Kawamura, Shusaku Egami, Koutarou Tamura, Yasunori Hokazono, Takanori Ugai, Yusuke Koyanagi, Fumihito Nishino, Seiji Okajima, Katsuhiko Murakami, Kunihiko Takamatsu, Aoi Sugiura, Shun Shiramatsu, Shawn Zhang, Kouji Kozaki

This paper presents a summary report of the first challenge held in 2018, including the knowledge graph construction, the techniques proposed for reasoning and/or estimation, the evaluation metrics, and the results.

graph construction