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Adversarial approaches in imaging: anomaly detection using GANs and image restoration techniques for inpainting and colorization. Explores efficient methods for unsupervised learning and data distribution analysis.
Explore probabilistic models of random monomial ideals, their properties, and applications in algebraic geometry. Gain insights into average behavior of ideals and potential for generating algebraic objects.
Exploring variational models as layers in deep neural networks for image segmentation, integrating traditional techniques with DNNs to improve performance and incorporate shape priors.
Explore the generic regularity in obstacle problems with Alessio Figalli. Learn about recent advancements in understanding singular sets and their codimension within free boundaries in elastic membrane equilibrium.
Explore nonlinear spectral decompositions in imaging and inverse problems, focusing on variational theory and applications in data science, with computational methods discussed.
Explore innovative wave scattering solutions in complex media using the Half-space Matching Method, offering efficient alternatives to traditional computational techniques.
Explore machine learning approaches for portfolio pricing and risk management in high-dimensional problems with expert Damir Filipovic.
Explore stochastic primal dual splitting algorithms for convex and nonconvex composite optimization in imaging, focusing on SPDFP and SVRG-PDFP methods and their applications.
Explore the intersection of camera imaging and cubic surfaces in this advanced seminar on multiview geometry, featuring insights into minimal reconstruction problems and their geometric implications.
Bayesian approach to sparse inverse problems in imaging and dictionary learning, featuring efficient computational framework and hierarchical prior models for sparsity promotion and automatic model reduction.
Explore machine learning approaches for portfolio pricing and risk management in high-dimensional problems with expert Damir Filipovic from EPFL and Swiss Finance Institute.
Explore Bayesian approaches to sparse inverse problems in imaging and dictionary learning, focusing on efficient computational frameworks and hierarchical prior models.
Applied mathematicians share their journeys to entrepreneurship, offering insights on career paths, challenges, and advice for aspiring math entrepreneurs in various industries.
Explore signal demixing using polar deconvolution, a novel approach for separating mixed signals with high efficiency and stability, presented by Michael Friedlander from UBC.
Explore fluctuation theory in the Boltzmann-Grad limit for hard-sphere gases, examining dynamical correlations and their implications for the kinetic Boltzmann equation and large deviation asymptotics.
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