Today's Arts & Science events
Active learning is an important concept in machine learning, in which the learning algorithm is able to choose where to query the underlying ground truth to improve the accuracy of the learned model. As machine learning techniques come to be more commonly used in scientific computing problems, where data is often expensive to obtain, the use of active learning is expected to be particularly important in the design of efficient algorithms. In this work, we introduce a general framework for active learning in regression problems. Our framework extends the standard setup by allowing for general types of data, rather than merely pointwise samples of the target function. This generalization covers many cases of practical interest, such as data acquired in transform domains (e.g., Fourier data), vector-valued data (e.g., gradient-augmented data), data acquired along continuous curves, and, multimodal data (i.e., combinations of different types of measurements). Our framework considers random sampling according to a finite number of sampling measures and arbitrary nonlinear approximation spaces (model classes). We introduce the concept of generalized Christoffel functions and show how these can be used to optimize the sampling measures. We prove that this leads to near-optimal sample complexity in various important cases. This work focuses on applications in scientific computing, where, as noted, active learning is often desirable, since it is usually expensive to generate data. We demonstrate the efficacy of our framework for gradient-augmented learning with polynomials, Magnetic Resonance Imaging (MRI) using generative models and adaptive sampling for solving PDEs using Physics-Informed Neural Networks (PINNs).
Upcoming Arts & Science events
Tu es un·e nouvel·le étudiant·e francophone qui entreprendra des études postsecondaires en langue anglaise pour la première fois à l'hiver 2024 ?
Please join us for a virtual conversation on anti-carceral movements and the university with professor and author Dr. Dan Berger.
The Informal Cities Working Group is promoted by the Center for Interdisciplinary Studies in Society and Culture (CISSC) from Concordia University.
Are you grappling with the overwhelming emotions that climate change brings?
Students with disabilities face unique challenges during clinical internship settings. The goal of this project is to facilitate the internship experiences of students with disabilities enrolled in Dawson College health-related, social service and community recreational leadership programs.
Tu es un nouvel·le étudiant·e francophone qui entreprendra des études postsecondaires en langue anglaise pour la première fois à l'hiver 2024 ?
Are you searching for a professional graduate program that dovetails your care for the environment and drive to make change with policies into a diverse career?
Join the Department of Economics in welcoming Senator Leo Housakos and Senator Tony Loffreda to Concordia for an armchair conversation about economic policy and governmental institutions.
The discussion aims to delve into the complex and evolving relationship between artificial intelligence and humanity.
The number of false positives in facial recognition technology has gone down dramatically.
Join us for a Dynamics and Number Theory workshop! June 5-7, 2024 Concordia University - LB 928 1455 De Maisonneuve Blvd. W. Montreal, QC Participants: Rob Benedetto (Amherst College), Laura DeMarco (Harvard), Vesselin Dimitrov (Georgia Institute of Technology), Andrea Ferraguti (University of Brescia), Patrick Ingram (York University), Nicole Looper (University of Illinois at Chicago), Myrto Mavraki (University of Toronto), Matt Olechnowicz (Concordia University), Carlo Pagano (Concordia University), Joe Silverman (Brown University), Umberto Zannier (Scuola Normale Superiore)
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