24 research outputs found
Dynamics of Generalized Tachyon Field in Teleparallel Gravity
We study dynamics of generalized tachyon scalar field in the framework of
teleparallel gravity. This model is an extension of tachyonic teleparallel dark
energy model which has been proposed in [26]. In contrast with tachyonic
teleparallel dark energy model that has no scaling attractors, here we find
some scaling attractors which means that the cosmological coincidence problem
can be alleviated. Scaling attractors present for both interacting and
non-interacting dark energy, dark matter cases.Comment: 14 pages, 20 figure
Dynamics of Interacting Tachyonic Teleparallel Dark Energy
We consider a tachyon scalar field which is nonminimally coupled to gravity in the framework of teleparallel gravity. We analyze the phase-space of the model, known as tachyonic teleparallel dark energy, in the presence of an interaction between dark energy and background matter. We find that although there exist some late-time accelerated attractor solutions, there is no scaling attractor. So, unfortunately interacting tachyonic teleparallel dark energy cannot alleviate the coincidence problem
Generative Mixture of Networks
A generative model based on training deep architectures is proposed. The
model consists of K networks that are trained together to learn the underlying
distribution of a given data set. The process starts with dividing the input
data into K clusters and feeding each of them into a separate network. After
few iterations of training networks separately, we use an EM-like algorithm to
train the networks together and update the clusters of the data. We call this
model Mixture of Networks. The provided model is a platform that can be used
for any deep structure and be trained by any conventional objective function
for distribution modeling. As the components of the model are neural networks,
it has high capability in characterizing complicated data distributions as well
as clustering data. We apply the algorithm on MNIST hand-written digits and
Yale face datasets. We also demonstrate the clustering ability of the model
using some real-world and toy examples.Comment: 9 page
Abstracts from the 8th International Conference on cGMP Generators, Effectors and Therapeutic Implications
This work was supported by a restricted research grant of Bayer AG