Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Vohra, Raunaq"'
Publikováno v:
Lecture Notes in Computer Science Volume 8681, 2014, pp 217-224
In this paper, we propose a generic technique to model temporal dependencies and sequences using a combination of a recurrent neural network and a Deep Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state of the RNN that all
Externí odkaz:
http://arxiv.org/abs/1412.7927
Publikováno v:
IEEE Xplore, Proceedings of IEEE SMC 2014, pages 4033 - 4034
This paper presents a versatile technique for the purpose of feature selection and extraction - Class Dependent Features (CDFs). We use CDFs to improve the accuracy of classification and at the same time control computational expense by tackling the
Externí odkaz:
http://arxiv.org/abs/1412.7934
In this paper, we present a novel brain computer interface based home automation system using two responses - Steady State Visually Evoked Potential (SSVEP) and the eye-blink artifact, which is augmented by a Bluetooth based indoor localization syste
Externí odkaz:
http://arxiv.org/abs/1412.7932
Autor:
Goel, Kratarth, Vohra, Raunaq
Since the advent of deep learning, it has been used to solve various problems using many different architectures. The application of such deep architectures to auditory data is also not uncommon. However, these architectures do not always adequately
Externí odkaz:
http://arxiv.org/abs/1412.6093
Publikováno v:
2015 IEEE International Conference on Data Science & Advanced Analytics (DSAA); 2015, p1-4, 4p
