Abstract: One of the long-unsolved open problems in machine learning is imbuing machine learning algorithms with human-like cognitive reasoning capabilities. An essential aspect of cognitive reasoning ...

Furthermore, domain adaptation (DA) has been the most common TL method in general, whereas inductive transfer learning (ITL) has been rare. To the best of our knowledge, DA and ITL have never been ...

Abstract: The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning ...

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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Young children develop sophisticated internal models of the world based on their visual experience. Can such models be learned from a child’s visual experience without strong inductive biases? To ...

In the ever-evolving world of machine learning, two fundamental approaches often come into play: inductive and transductive learning. While both methods are instrumental in how machines interpret and ...

Machine learning, the cornerstone of artificial intelligence, is empowered by its ability to learn patterns and make predictions from data. At its core lies the concept of inductive inference, a ...