Finite mixture distributions arise naturally in many applications including clustering and classification. Since they usually do not yield closed forms for maximum likelihood estimates (MLEs), numerical methods using the well known Fisher Scoring or Expectation-Maximization algorithms are considered. In this work, an approximation to the Fisher Information Matrix of an arbitrary mixture of mult...

Recent progress using geometry in the design of efficient Markov chain Monte Carlo (MCMC) algorithms have shown the effectiveness of the Fisher Riemannian structure. Furthermore, the theory of the underlying geometry of spaces of statistical models has made an important breakthrough by extending the classical theory on exponential families to their closures, the so-called extended exponential f...

1. Dussault, J. H., Coulombe, P., Laberge, C., et al., Preliminary report on a mass screening program for neonatal hypothyroidism. J. Pediatr. 86, 670 (1975). 2. Larsen, P. R., and Broskin, K., Throxine (T-4) immunoassay using filter paper blood samples for screening neonates for hypothyroidism. Pediatr. Res. 9, 604 (1975). 3. Walfish, P. G., Screening for neonatal hypothyroidism using a dried ...

One approach to improve the accuracy of classifications based on generative models is to combine them with successful discriminative algorithms. Fisher kernels were developed to combine generative models with a currently very popular class of learning algorithms, kernel methods. Empirically, the combination of hidden Markov models with support vector machines has shown promising results. So far...

This paper addresses the problem of large scale image retrieval, with the aim of accurately ranking the similarity of a large number of images to a given query image. To achieve this, we propose a novel Siamese network. This network consists of two computational strands, each comprising of a CNN component followed by a Fisher vector component. The CNN component produces dense, deep convolutiona...

Generative Adversarial Networks (GANs) are powerful models for learning complex distributions. Stable training of GANs has been addressed in many recent works which explore different metrics between distributions. In this paper we introduce Fisher GAN which fits within the Integral Probability Metrics (IPM) framework for training GANs. Fisher GAN defines a critic with a data dependent constrain...