4,636 research outputs found
Cruising The Simplex: Hamiltonian Monte Carlo and the Dirichlet Distribution
Due to its constrained support, the Dirichlet distribution is uniquely suited
to many applications. The constraints that make it powerful, however, can also
hinder practical implementations, particularly those utilizing Markov Chain
Monte Carlo (MCMC) techniques such as Hamiltonian Monte Carlo. I demonstrate a
series of transformations that reshape the canonical Dirichlet distribution
into a form much more amenable to MCMC algorithms.Comment: 5 pages, 0 figure
Nested Sampling with Constrained Hamiltonian Monte Carlo
Nested sampling is a powerful approach to Bayesian inference ultimately
limited by the computationally demanding task of sampling from a heavily
constrained probability distribution. An effective algorithm in its own right,
Hamiltonian Monte Carlo is readily adapted to efficiently sample from any
smooth, constrained distribution. Utilizing this constrained Hamiltonian Monte
Carlo, I introduce a general implementation of the nested sampling algorithm.Comment: 15 pages, 4 figure
Detecting Hands in Egocentric Videos: Towards Action Recognition
Recently, there has been a growing interest in analyzing human daily
activities from data collected by wearable cameras. Since the hands are
involved in a vast set of daily tasks, detecting hands in egocentric images is
an important step towards the recognition of a variety of egocentric actions.
However, besides extreme illumination changes in egocentric images, hand
detection is not a trivial task because of the intrinsic large variability of
hand appearance. We propose a hand detector that exploits skin modeling for
fast hand proposal generation and Convolutional Neural Networks for hand
recognition. We tested our method on UNIGE-HANDS dataset and we showed that the
proposed approach achieves competitive hand detection results
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