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Circuit Learning for Logic Regression on High Dimensional Boolean Space | IEEE Conference Publication | IEEE Xplore

Circuit Learning for Logic Regression on High Dimensional Boolean Space


Abstract:

Logic regression aims to find a Boolean model involving binary covariates that predicts the response of an unknown system. It has many important applications, e.g., in da...Show More

Abstract:

Logic regression aims to find a Boolean model involving binary covariates that predicts the response of an unknown system. It has many important applications, e.g., in data analysis and system design. In the 2019 ICCAD CAD Contest, the challenge of learning a compact circuit representing a black-box input-output pattern generator in a high dimensional Boolean space is formulated as the logic regression problem. This paper presents our winning approach to the problem based on a decision-tree reasoning procedure assisted with a template based preprocessing. Our methods outperformed other contestants in the competition in both prediction accuracy and circuit size.
Date of Conference: 20-24 July 2020
Date Added to IEEE Xplore: 09 October 2020
ISBN Information:
Print on Demand(PoD) ISSN: 0738-100X
Conference Location: San Francisco, CA, USA

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