17 research outputs found
Fast Neural Network Ensemble Learning via Negative-Correlation Data Correction
This letter proposes a new negative correlation (NC) learning method that is both easy to implement and has the advantages that: 1) it requires much lesser communication overhead than the standard NC method and 2) it is applicable to ensembles of heterogenous networks. © 2005 IEEE
Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble
Motivation: So far various bioinformatics and machine learning techniques applied for identification of sequence and functionally conserved residues in proteins. Although few computational methods are available for the prediction of structurally conserved residues from protein structure, almost all methods require homologous structural information and structure-based alignments, which still prove to be a bottleneck in protein structure comparison studies. In this work, we developed a neural network approach for identification of structurally important residues from a single protein structure without using homologous structural information and structural alignment
Deep Prediction Of Investor Interest: a Supervised Clustering Approach
We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given timeframe. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a simulated scenario inspired by real data and then apply it to a large proprietary database from BNP Paribas Corporate and Institutional Banking
Towards Better Accuracy-efficiency Trade-offs: Divide and Co-training
The width of a neural network matters since increasing the width will
necessarily increase the model capacity. However, the performance of a network
does not improve linearly with the width and soon gets saturated. In this case,
we argue that increasing the number of networks (ensemble) can achieve better
accuracy-efficiency trade-offs than purely increasing the width. To prove it,
one large network is divided into several small ones regarding its parameters
and regularization components. Each of these small networks has a fraction of
the original one's parameters. We then train these small networks together and
make them see various views of the same data to increase their diversity.
During this co-training process, networks can also learn from each other. As a
result, small networks can achieve better ensemble performance than the large
one with few or no extra parameters or FLOPs. Small networks can also achieve
faster inference speed than the large one by concurrent running on different
devices. We validate our argument with 8 different neural architectures on
common benchmarks through extensive experiments. The code is available at
\url{https://github.com/mzhaoshuai/Divide-and-Co-training}
A comparative study of surrogate musculoskeletal models using various neural network configurations
Title from PDF of title page, viewed on August 13, 2013Thesis advisor: Reza R. DerakhshaniVitaIncludes bibliographic references (pages 85-88)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2013The central idea in musculoskeletal modeling is to be able to predict body-level
(e.g. muscle forces) as well as tissue-level information (tissue-level stress, strain, etc.). To
develop computationally efficient techniques to analyze such models, surrogate models
have been introduced which concurrently predict both body-level and tissue-level
information using multi-body and finite-element analysis, respectively. However, this
kind of surrogate model is not an optimum solution as it involves the usage of finite
element models which are computation intensive and involve complex meshing methods
especially during real-time movement simulations. An alternative surrogate modeling
method is the use of artificial neural networks in place of finite-element models. The ultimate objective of this research is to predict tissue-level stresses
experienced by the cartilage and ligaments during movement and achieve concurrent
simulation of muscle force and tissue stress using various surrogate neural network
models, where stresses obtained from finite-element models provide the frame of
reference. Over the last decade, neural networks have been successfully implemented in
several biomechanical modeling applications. Their adaptive ability to learn from
examples, simple implementation techniques, and fast simulation times make neural networks versatile and robust when compared to other techniques. The neural network
models are trained with reaction forces from multi-body models and stresses from finite
element models obtained at the interested elements. Several configurations of static and
dynamic neural networks are modeled, and accuracies close to 93% were achieved, where
the correlation coefficient is the chosen measure of goodness. Using neural networks, the
simulation time was reduced nearly 40,000 times when compared to the finite-element
models. This study also confirms theoretical concepts that special network
configurations--including average committee, stacked generalization, and negative
correlation learning--provide considerably better results when compared to individual
networks themselves.Introduction -- Methods -- Results -- Conclusion -- Future work -- Appendix A. Various linear and non-linear modeling techniques -- Appendix B. Error analysi
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