no code implementations • 2 Aug 2021 • Alain Oliviero Durmus, Andreas Eberle
Inexact Markov Chain Monte Carlo methods rely on Markov chains that do not exactly preserve the target distribution.
no code implementations • 3 May 2021 • Nawaf Bou-Rabee, Andreas Eberle
We provide quantitative upper bounds on the total variation mixing time of the Markov chain corresponding to the unadjusted Hamiltonian Monte Carlo (uHMC) algorithm.
no code implementations • 29 Sep 2020 • Nawaf Bou-Rabee, Andreas Eberle
Andersen dynamics is a standard method for molecular simulations, and a precursor of the Hamiltonian Monte Carlo algorithm used in MCMC inference.
no code implementations • 1 May 2018 • Nawaf Bou-Rabee, Andreas Eberle, Raphael Zimmer
Based on a new coupling approach, we prove that the transition step of the Hamiltonian Monte Carlo algorithm is contractive w. r. t.
1 code implementation • 23 Mar 2018 • Andreas Eberle
In this work, an effective approach to integrate coarse camera view information as well as fine-grained pose information into a convolutional neural network (CNN) model for learning discriminative re-id embeddings is introduced.
2 code implementations • CVPR 2018 • M. Saquib Sarfraz, Arne Schumann, Andreas Eberle, Rainer Stiefelhagen
In contrast to the recent direction of explicitly modeling body parts or correcting for misalignment based on these, we show that a rather straightforward inclusion of acquired camera view and/or the detected joint locations into a convolutional neural network helps to learn a very effective representation.
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