Supplementary Material for Efficient and Robust Automated Machine Learning
Supplementary Material for Efficient and Robust Automated Machine Learning
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Supplementary material for Uncorrected least-squares temporal difference with lambda-return
Here, we provide a supplementary material for Takayuki Osogami, "Uncorrected least-squares temporal difference with lambda-return," which appears in {\it Proceedings of the 34th AAAI Conference on Artificial Intelligence…
Machine Learning-based Prediction of Porosity for Concrete Containing Supplementary Cementitious Materials
Porosity has been identified as the key indicator of the durability properties of concrete exposed to aggressive environments. This paper applies ensemble learning to predict porosity of high-performance concrete contain…
BIG-bench Machine LearningEnsemble LearningMetaScientist: A Human-AI Synergistic Framework for Automated Mechanical Metamaterial Design
The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resource-demanding process. To accelerate the …
validProofs and Supplementary Material: Unified Characterization and Precoding for Non-Stationary Channels
This document provides the supplementary material including a comprehensive related work, the complete proofs and extended evaluation results that support the manuscript, "Unified Characterization and Precoding for Non-S…
LEMMAA Collective, Probabilistic Approach to Schema Mapping: Appendix
In this appendix we provide additional supplementary material to "A Collective, Probabilistic Approach to Schema Mapping." We include an additional extended example, supplementary experiment details, and proof for the co…