Grants and Contracts Details
Description
Overview:
Deep learning, or deep neural network, has emerged over the last decade as one of the most powerful
machine learning methods. Recurrent neural network (RNN) is a special architecture that is designed to
efficiently model sequential data such as speech and text by exploring temporal connections of the
sequence and allowing variable length of the input sequence. It suffers, however, from so-called
vanishing or exploding gradient problems. Its regularization for better generalization from training data to
testing data has also been shown to be challenging. This project will develop an efficient and robust
recurrent neural network by systematically addressing its various limitations.
Intellectual Merit:
Developing a robust and theoretically simple RNN is an intellectually challenging problem. The current
preferred RNN architecture, the Long Short Term Memory network, has a highly complex structure with
numerous additional interacting elements that is not well understood and not easy to implement. Our
proposed network will retain the theoretical simplicity and efficiency of the basic RNN architecture but
enhance some key capabilities for robust implementations. This has the potential to significantly advance
the state of the art in the theory and algorithms of RNNs.
Broader Impacts:
The results from the proposed research are expected to impact a variety of areas involving sequential
data. Computer vision, speech recognition, natural language processing, financial data analysis, and
bioinformatics are some examples, and this list is expanding rapidly. This project will expand the
applicability and functionality of RNNs, which may popularize it to a larger user community. The
proposed research lies at the interface between applied mathematics, computer science, and statistics and
provides an ideal setting for research cross-fertilization and collaboration as well as training of graduate
students in interdisciplinary research. In this regard, the perspective from numerical analysis point of
view will be particularly helpful in approaching various problems in neural networks. The project will
also include collaborative works to apply the RNNs developed to the RNA secondary structure inference
problems in bioinformatics. We plan to share computer codes derived in this project in the open source
platform GitHub, which will accelerate dissemination of the research results to the user communities and
promote real-world applications of RNNs.
Status | Finished |
---|---|
Effective start/end date | 9/1/18 → 8/31/22 |
Funding
- National Science Foundation: $200,000.00
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