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Found 6 papers, 2 papers with code
Asymptotic bias of inexact Markov Chain Monte Carlo methods in high dimension
Inexact Markov Chain Monte Carlo methods rely on Markov chains that do not exactly preserve the target distribution.
Mixing Time Guarantees for Unadjusted Hamiltonian Monte Carlo
We provide quantitative upper bounds on the total variation mixing time of the Markov chain corresponding to the unadjusted Hamiltonian Monte Carlo (uHMC) algorithm.
Couplings for Andersen Dynamics
Andersen dynamics is a standard method for molecular simulations, and a precursor of the Hamiltonian Monte Carlo algorithm used in MCMC inference.
Coupling and Convergence for Hamiltonian Monte Carlo
Based on a new coupling approach, we prove that the transition step of the Hamiltonian Monte Carlo algorithm is contractive w. r. t.
Pose-Driven Deep Models for Person Re-Identification
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.
A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking
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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Person Re-Identification
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