Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Prayag Gowgi"'
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:4900-4914
We develop a systematic theory to reconstruct missing samples in a time series using a spatiotemporal memory based on artificial neural networks. The Markov order of the input process is learned and subsequently used for learning temporal correlation
Publikováno v:
IJCNN
Self-organizing maps (SOM) are popularly used for applications in learning features, vector quantization and recalling spatial input patterns. The adaptation rule in SOM is based on the Euclidean distance between the input vector and the neuronal wei
Autor:
Vijaya Yajnanarayana, Prayag Gowgi
A typical handover problem requires sequence of complex signaling between a UE, the serving, and target base station. In many handover problems the down link based measurements are transferred from a user equipment to a serving base station and the d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64ff319367bd416ca1435ee9f16e4f55
http://arxiv.org/abs/2103.15318
http://arxiv.org/abs/2103.15318
Autor:
Shayan Srinivasa Garani, Prayag Gowgi
Publikováno v:
IJCNN
Learning rate is a crucial parameter governing the convergence rate of any learning algorithm. Most of the learning algorithms based on stochastic gradient descent (SGD) method depend on heuristic choice of learning rate. In this paper, we derive bou
Autor:
Prayag Gowgi, Shayan Srinivasa Garani
Publikováno v:
IEEE transactions on neural networks and learning systems. 30(2)
Self-organizing maps (SOMs) find numerous applications in learning, clustering, and recalling spatial input patterns. The traditional approach in learning spatiotemporal patterns is to incorporate time on the output space of a SOM along with heuristi
Publikováno v:
IJCNN
Vector quantization techniques using self-organizing maps (SOM) and its variants are popularly used for applications in contextual data clustering, data visualization and high-dimensional data exploration. The update rule in a SOM is based on competi
Autor:
Shayan Garani Srinivasa, Prayag Gowgi
Publikováno v:
IJCNN
We look at the neural network as a non-linear probability density function (pdf) transformer by stochastic learning cumulative (SLC) technique. We formulate a potential function that drives a neural network to non-linearly transform the input pdf to
Autor:
Prayag Gowgi, Shayan Garani Srinivasa
Publikováno v:
IJCNN
We revisit the problem of temporal self organization using activity diffusion based on the neural gas (NGAS) algorithm. Using a potential function formulation motivated by a spatio-temporal metric, we derive an adaptation rule for dynamic vector quan