Objectives Elective non-emergent surgical wait times have increased across countries such as Canada, straining operating room ...

Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...

Abstract: Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be ...

Abstract: The emergence of big data has enabled the creation of significant models by allowing the storage of large data volumes. Transfer learning is a machine learning technique that transfers ...

Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification. This repository is made for you if: you're new to few-shot learning and want to learn; or you're looking ...

And how to catch up if you’re lagging behind by Ajay Agrawal, Joshua Gans and Avi Goldfarb The past decade has brought tremendous advances in an exciting dimension of artificial intelligence—machine ...

B.S. in Computer Engineering, University of Illinois at Urbana/Champaign, 1983 M.S. in Computer Science, University of Illinois at Urbana/Champaign, 1985 See my invited talk at the EMNLP 2023 Big ...

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Graph-structured data is crucial for modeling complex real-world systems, but traditional machine learning struggles with non-Euclidean relationships inherent in graphs. Graph embedding techniques ...