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Raunaq Vohra

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A Novel Feature Selection and Extraction Technique for Classification

Dec 26, 2014
Kratarth Goel, Raunaq Vohra, Ainesh Bakshi

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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 curse of dimensionality. In order to demonstrate the generality of this technique, it is applied to handwritten digit recognition and text categorization.

* IEEE Xplore, Proceedings of IEEE SMC 2014, pages 4033 - 4034  
* 2 pages, 2 tables, published at IEEE SMC 2014 
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Polyphonic Music Generation by Modeling Temporal Dependencies Using a RNN-DBN

Dec 26, 2014
Kratarth Goel, Raunaq Vohra, J. K. Sahoo

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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 allows it to provide temporal information and a multi-layer DBN that helps in high level representation of the data. This makes RNN-DBNs ideal for sequence generation. Further, the use of a DBN in conjunction with the RNN makes this model capable of significantly more complex data representation than an RBM. We apply this technique to the task of polyphonic music generation.

* Lecture Notes in Computer Science Volume 8681, 2014, pp 217-224  
* 8 pages, A4, 1 figure, 1 table, ICANN 2014 oral presentation. arXiv admin note: text overlap with arXiv:1206.6392 by other authors 
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Learning Temporal Dependencies in Data Using a DBN-BLSTM

Dec 23, 2014
Kratarth Goel, Raunaq Vohra

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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 consider the temporal dependencies in data. We thus propose a new generic architecture called the Deep Belief Network - Bidirectional Long Short-Term Memory (DBN-BLSTM) network that models sequences by keeping track of the temporal information while enabling deep representations in the data. We demonstrate this new architecture by applying it to the task of music generation and obtain state-of-the-art results.

* 6 pages, 2 figures, 1 table, ICLR 2015 conference track submission under review 
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