Colloquia in 2024
Statistical analysis of SPDEs – exploring singularities
Igor Cialenco, Illinois Institute of Technology 2.28.0.10810:15-11:15
Unlike traditional finite-dimensional stochastic differential equations, statistical models driven by SPDEs are predominantly singular, when the…
more ›From Neurons to Symbolic Representation and Rapid Learning in Mind and Brain: A Brain-Constrained Neural Model
Rosario Tomasello, FU Berlin 2.28.0.10810:15-11:15
To grasp the meaning of words and their relationship to the outside world, higher cognitive processes unique to the human brain are at work. However,…
more ›Investigating time-series and spatial fields from environmental archives to inform about past and future dynamics of climate and biodiversity
Ulrike Herzschuh & Thomas Laepple, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research 2.28.0.10810:15 - 11:45
Our planet is undergoing an unprecedentedly rapid transformation. To predict future climate and environment dynamics, we have to learn from past…
more ›Kernel Methods in Generative Modeling
Gabriele Steidl, TU Berlin tba10:15 - 11:45
We consider gradient flows with respect to the maximum mean discrepancy (MMD) of certain kernels. For the efficient computation, we propose slicing…
more ›Random walks of heterogeneous population and ensemble self-reinforcement
Sergei Fedotov, University of Manchester 2.28.0.10810:15 - 11:45
The talk will be concerned with time-fractional master equations with random transition probabilities describing a heterogeneous population of random…
more ›Is dispersion a stabilizing or destabilizing mechanism? Landau-damping induced by fast background flows
Edriss Titi , University of Cambridge 2.28.0.10810:15 - 11:45
In this talk, I will present a unifed approach for the effect of fast rotation and dispersion as an averaging mechanism for regularizing and…
more ›Particle Methods in Machine Learning and Inverse Problems
Martin Burger, Helmholtz Imaging 3.06.S1814:30 - 16:00
7th Kálmán Lecture with Martin Burger
The use of methods resembling (interacting) particle systems has gained a lot of interest for different tasks…
more ›Scalable methods for Gaussian process regression
Botond Tibor Szabo, Bocconi University, Italy 2.28.0.10810:15 - 11:45
Gaussian processes (GP) are frequently used in Bayesian nonparametrics as a prior distribution on infinite dimensional functional parameters. However,…
more ›A tensor bidiagonalization method for singular value decomposition of third order tensors
Lothar Reichel & Laura Dykes, Kent State University 2.28.0.10810:15 - 11:45
The need to know a few singular triplets associated with the largest singular values of a third-order tensor arises in data compression and extraction. This paper describes a new method for their computation using the t-product. Methods for deter mining a couple of singular triplets associated with the smallest singular values also are presented. The proposed methods generalize available restarted Lanczos bidiagonalization methods for computing a few of the largest or smallest singular triplets of a matrix. The methods of this paper use Ritz and harmonic Ritz lateral slices to determine accurate approximations of the largest and smallest singular triplets, respectively. Computed examples show applications to data compression and face recognition. more ›
State-space models as graphs
Víctor Elvira, University of Edinburgh 2.28.0.10810:15 - 11:45
Modeling and inference in multivariate time series is central in statistics, signal processing, and machine learning. A fundamental question when…
more ›Bayesian chemotaxis: Information theory of chemotaxis agents combining spatial and temporal gradient-sensing
Benjamin M. Friedrich, TU Dresden 2.28.0.10810:15 - 11:45
Biological systems process information despite noise-corrupted input, often operating at physical limits. A prime example is chemotaxis, i.e., active…
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