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arXiv:1412.6093cs.LG

Learning Temporal Dependencies in Data Using a DBN-BLSTM

Kratarth Goel, Raunaq Vohra

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Abstract

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.

arXiv comments
6 pages, 2 figures, 1 table, ICLR 2015 conference track submission under review
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.NENeural and Evolutionary Computing

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Linked by 1 identifiable commenter in 2 comments across 2 threads, from May 2015 to Aug 2015.

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Discussed in comments2 threads

  1. Classical music generation with recurrent neural networks 1 comment

    • This is really cool stuff - the network structure reminds me a lot of Graves' MDRNN[1] and Grid LSTM[2], as well as some work I helped with (ReNet [3]) I wonder if the structure over frequency/time is too "regular" - in general for sound the frequency correlation and the time…

      kastnerkyleView on HNParent context

  2. The Unreasonable Effectiveness of Recurrent Neural Networks 1 comment

    • The work of Nicolas Boulanger-Lewandowski was extensively focused on this topic, see his work [1]. He wrote a Theano deep learning tutorial on this topic [2], and several people (Kratarth Goel) [3][4] have advanced the work to use LSTM and deep belief networks. For a brief while…

      kastnerkyleView on HNParent context

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