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Animated heads from ordinary images: a least-squares approach
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This paper analyzes the implementation of least-mean-squares (LMS)-based, adaptive diffusion algorithms over networks in the frequency-domain (FD). We focus on a scenario of noisy links and include a moving-average step for denoising after self-learning to ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021Superluminal Motion-Assisted Four-Dimensional Light-in-Flight Imaging
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AMER PHYSICAL SOC2021Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates
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2020On raw inertial measurements in dynamic networks
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2019Basic gestalt laws cannot explain uncrowding
Michael Herzog, Oh-Hyeon Choung, Einat Rashal
Visual crowding is the inability to perceive elements within clutter. Traditional crowding models, such as pooling, predict that performance deteriorates when flankers are added. However, this prediction has been disproved. For example, performance was fou ...
SAGE PUBLICATIONS LTD2019Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
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ICML2018Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization
We propose two new alternating direction methods to solve “fully” nonsmooth constrained convex problems. Our algorithms have the best known worst-case iteration-complexity guarantee under mild assumptions for both the objective residual and feasibility gap ...
Springer2018Reconstruction of 3D scattered data via radial basis functions by efficient and robust techniques
We propose new algorithms to overcome two of the most constraining limitations of surface reconstruction methods in use. In particular, we focus on the large amount of data characterizing standard acquisitions by scanner and the noise intrinsically introdu ...
Elsevier Science Bv2017Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
Nicolas Henri Bernard Flammarion, Aymeric Daphnis Kévin Dieuleveut
We consider the optimization of a quadratic objective function whose gradients are only accessible through a stochastic oracle that returns the gradient at any given point plus a zero-mean finite variance random error. We present the first algorithm that a ...
2017