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- research-articleNovember 2024
BNN-YEO: an efficient Bayesian Neural Network for yield estimation and optimization
DAC '24: Proceedings of the 61st ACM/IEEE Design Automation ConferenceArticle No.: 231, Pages 1–6https://doi.org/10.1145/3649329.3658242Yield estimation and optimization is ubiquitous in modern circuit design but remains elusive for large-scale chips. This is largely due to the mounting cost of transistor-level simulation and one's often limited resources. In this study, we propose a ...
- ArticleNovember 2023
CD-BNN: Causal Discovery with Bayesian Neural Network
AbstractCausal discovery involves learning Directed Acyclic Graphs (DAGs) from observational data and has widespread applications in various fields. Recent advancements in the structural equation model (SEM) have successfully applied continuous ...
- research-articleSeptember 2023
AI Based Solution to Optimize the Fertilizer Composition in Hydroponics Agriculture
ICMLC '23: Proceedings of the 2023 15th International Conference on Machine Learning and ComputingPages 158–164https://doi.org/10.1145/3587716.3587742Precision agriculture is one of the trending research areas in the world. AI technologies have been applied to greenhouse hydroponics agriculture (which comes under precision agriculture) to control the requirements of the plants in greenhouse ...
- research-articleMay 2022
ANUBIS: a provenance graph-based framework for advanced persistent threat detection
SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied ComputingPages 1684–1693https://doi.org/10.1145/3477314.3507097We present ANUBIS, a highly effective machine learning-based APT detection system. Our design philosophy for ANUBIS involves two principal components. Firstly, we intend ANUBIS to be effectively utilized by cyber-response teams. Therefore, prediction ...