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When Attention Meets Big Data and Deep Learning
Description:
We develop computational and experimental methods to gain insights into visual functions and psychiatric disorders. We also build deep learning models that predict human behaviors and identify people with disorders. In this talk, I will share our recent innovations on data and models, aiming at understanding and predicting visual attention in na...
Creator:
Zhao, Catherine Qi (University of Minnesota, Twin Cities)
Created:
2017-02-03
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
When Attention Meets Big Data and Deep Learning
Semi-parametric Dynamic Max-copula Model for Multivariate Time Series
Description:
This paper presents a novel nonlinear framework for the construction of flexible multivariate dependence structure~(i.e., copula) from existing copulas based on a straightforward "pairwise max" rule. The newly constructed max-copula has a closed form and has strong interpretability. Compared to the classical "linear symmetric" mixture copula, th...
Creator:
Zhang, Zhengjun (University of Wisconsin, Madison)
Created:
2018-02-21
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Semi-parametric Dynamic Max-copula Model for Multivariate Time Series
American Option Pricing Models and Obstacle Problems
Description:
We first give a brief overview of American option pricing models and numerical methods. We treat American option models as a special class of obstacle problems. Finite element formulation is introduced together with error analysis of numerical solutions. Some interesting properties about sensitivity of the option price to the payoff function are...
Creator:
Zhang, Yongmin (NONE)
Created:
2009-02-27
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
American Option Pricing Models and Obstacle Problems
Remembering and forgetting in a disconnecting air bubble
Description:
Studies on the break-up of a liquid drop or an air bubble reveal that the dynamics prior to a singularity can have several forms, ranging from universal, with no memory of the initial state, or integrable, which has a complete memory. We find that how an air bubble disconnects from an underwater nozzle is associated with an unusually rich class ...
Creator:
Zhang, Wendy W. (University of Chicago)
Created:
2008-07-18
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
Remembering and forgetting in a disconnecting air bubble
Orthogonal Matrix Retrieval in Cryo-electron Microscopy
Description:
In single particle reconstruction (SPR) from cryo-electron microscopy (EM), the 3D structure of a molecule needs to be determined from its 2D projection images taken at unknown viewing directions. Zvi Kam showed already in 1980 that the autocorrelation function of the 3D molecule over the rotation group SO(3) can be estimated from 2D projection ...
Creator:
Zhang, Teng (University of Central Florida)
Created:
2016-11-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
Orthogonal Matrix Retrieval in Cryo-electron Microscopy
First-Order Algorithms
Description:
In this talk we will discuss low-complexity algorithms for solving large-scale convex optimization problems. Such algorithms include: gradient projection, proximal gradient, Iterative Shrinkage-Thresholding (ISTA), Nesterov's acceleration, and Alternating Direction Method of Multipliers (ADMM). The emphasis of the discussion will be placed on th...
Creator:
Zhang, Shuzhong (University of Minnesota, Twin Cities)
Created:
2016-08-01
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
First-Order Algorithms
Semidefinite Programming
Description:
In this talk we will introduce conic optimization models, in particular second-order cone programming (SOCP) and semidefinite programming (SDP). We will discuss various applications of these new optimization models. Conic duality theory will be introduced as well. Finally we will introduce the so-called central path following method for solving ...
Creator:
Zhang, Shuzhong (University of Minnesota, Twin Cities)
Created:
2016-08-03
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
Semidefinite Programming
Seminar: Data Recovery, Information Revealing, Tensor Completion and Optimization
Description:
In this talk we discuss computational issues related to low-rank tensor completion and decomposition problems. Computing the so-called CP rank of a given tensor is already notoriously difficult, let alone the completion or decomposition models where the CP-rank plays the role of a regularization function. In this talk, we introduce various matri...
Creator:
Zhang, Shuzhong (University of Minnesota, Twin Cities)
Created:
2018-05-17
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Seminar: Data Recovery, Information Revealing, Tensor Completion and Optimization
Tensor Low-Rank Completion and Invariance of the Tucker Core
Description:
This talk presents two parts of results related to tensor computations. The first part is on a new matricization approach, resulting in lower and upper approximations for the CP-rank. In this part, theoretical properties of the new ranks (to be called the M-ranks) will be discussed, with applications to solve the low CP-rank tensor completion pr...
Creator:
Zhang, Shuzhong (University of Minnesota, Twin Cities)
Created:
2016-01-28
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
Tensor Low-Rank Completion and Invariance of the Tucker Core
Yu Chen
Creator:
Zhang, Radium
Contributor:
Rolland, Isabella
Created:
2020-04-21
Contributed By:
University of Minnesota, Immigration History Research Center
Last Updated:
2021-01-26
Yu Chen
Hierarchical deep generative networks for Bayesian inverse problems
Description:
Deep generative networks have achieved great success in high dimensional density approximation, especially for approximating the distributions of natural images and languages. In this talk, we propose to leverage their approximation capability to approximate posterior distributions in Bayesian Inverse Problems (BIPs). To train deep generative ne...
Creator:
Zhang, Pengchuan (Microsoft Research)
Created:
2018-10-22
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Hierarchical deep generative networks for Bayesian inverse problems
C-learning: A New Classification Framework to Estimate Optimal Dynamic Treatment Regimes
Description:
A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual’s own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem ...
Creator:
Zhang, Min (University of Michigan)
Created:
2018-11-09
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
C-learning: A New Classification Framework to Estimate Optimal Dynamic Treatment Regimes
Bezier Subdivision for Inverse Molecular Kinematics
Description:
Conformational searching is a core task in inverse molecularkinematics. Algorithmic improvements affecting either the speed orquality of conformational searching will have a profound impact onapplications including ligand-receptor docking, ab initio predictionof protein structure, and protein folding. In this talk, we focus on aspecific geometry...
Creator:
Zhang, Ming (University of Texas)
Created:
2007-06-01
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
Bezier Subdivision for Inverse Molecular Kinematics
Meng Zhang
Description:
Meng Zhang was born in Xi’an, China in 1987. She graduated from Xi’an Polytechnic University. She and her family came to the United States on February 14, 2014.
Creator:
Zhang, Meng
Created:
2015-03-20 - 2015-06-20
Contributed By:
University of Minnesota, Immigration History Research Center
Last Updated:
2021-01-26
Meng Zhang
Information Geometry: From Divergence Function to Metric, Equiaffine, Symplectic, and Kahler Structures on a Manifold
Description:
Divergence functions, as a proximity measure on a smooth manifold and often surrogate to the (symmetric) metric function, play an important role in machine learning, statistical inference, optimization, etc. This talk will review the various geometric structures induced from a divergence function defined on a manifold. Most importantly, a Rieman...
Creator:
Zhang, Jun (University of Michigan)
Created:
2013-10-28
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
Information Geometry: From Divergence Function to Metric, Equiaffine, Symplectic, and Kahler Structures on a Manifold
Unidirectional Forward Flight of a Flapping Wing
Description:
The locomotion of most fish and birds is realized by flapping wings or fins transverse to the direction of travel. Here, we study experimentally the dynamics of a wing that is flapped up and down but is free to move in the horizontal direction. In this table-top prototype experiment, we show that flapping flight occurs abruptly at a critical fla...
Creator:
Zhang, Jun (New York University)
Created:
2006-06-28
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
Unidirectional Forward Flight of a Flapping Wing
Martingale representation theorem for the G-expectation
Description:
In recent years Peng prososed a new notion called G-expectation, a type of nonlinear expectation motivated from dynamic risk measures with volatility uncertainty. On the other hand, a martingale under the G-expectation can be viewed as the solution to a 'linear' Second Order Backward SDEs, the main subject of the short course which will be given...
Creator:
Zhang, Jianfeng (University of Southern California)
Created:
2010-06-14
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
Martingale representation theorem for the G-expectation
Viscosity Solutions to Parabolic Master Equations
Description:
Master equation is a powerful tool for studying McKean-Vlasov dynamics where the distribution of the state process enters the coefficients directly, with particular applications including mean field games and stochastic control problems with partial information. In this talk we propose an intrinsic notion of viscosity solution for parabolic mast...
Creator:
Zhang, Jianfeng (University of Southern California)
Created:
2018-06-13
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Viscosity Solutions to Parabolic Master Equations
Optimal Gradient Methods for Nonlinear Optimization
Description:
In this talk, we will discuss extending the optimal gradient methods for solving convex optimization to deal with more general nonlinear, possibly nonconvex and nonsmooth, optimization problems. These algorithms will treat the nonconvex and convex optimization problems in a unified way so that they will achieve the best known complexity for solv...
Creator:
Zhang, Hongchao (Louisiana State University)
Created:
2016-01-26
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
Optimal Gradient Methods for Nonlinear Optimization
Fluid Models of Parallel Service Systems under FCFS
Description:
We study deterministic fluid approximation models of parallel service systems, operating under first come first served policy (FCFS), when the service time distributions may depend on both the server and the customer type. We explore the relations between fluid models and the properties of stability, resource pooling, and matching rates. We find...
Creator:
Zhang, Hanqin (National University of Singapore)
Created:
2018-05-18
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Fluid Models of Parallel Service Systems under FCFS