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  1. Book ; Online: Lie Group Auto-Encoder

    Gong, Liyu / Cheng, Qiang

    2019  

    Abstract: In this paper, we propose an auto-encoder based generative neural network model whose encoder compresses the inputs into vectors in the tangent space of a special Lie group manifold: upper triangular positive definite affine transform matrices (UTDATs). ... ...

    Abstract In this paper, we propose an auto-encoder based generative neural network model whose encoder compresses the inputs into vectors in the tangent space of a special Lie group manifold: upper triangular positive definite affine transform matrices (UTDATs). UTDATs are representations of Gaussian distributions and can straightforwardly generate Gaussian distributed samples. Therefore, the encoder is trained together with a decoder (generator) which takes Gaussian distributed latent vectors as input. Compared with related generative models such as variational auto-encoder, the proposed model incorporates the information on geometric properties of Gaussian distributions. As a special case, we derive an exponential mapping layer for diagonal Gaussian UTDATs which eliminates matrix exponential operator compared with general exponential mapping in Lie group theory. Moreover, we derive an intrinsic loss for UTDAT Lie group which can be calculated as l-2 loss in the tangent space. Furthermore, inspired by the Lie group theory, we propose to use the Lie algebra vectors rather than the raw parameters (e.g. mean) of Gaussian distributions as compressed representations of original inputs. Experimental results verity the effectiveness of the proposed new generative model and the benefits gained from the Lie group structural information of UTDATs.
    Keywords Computer Science - Machine Learning ; Mathematics - Group Theory ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2019-01-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Exploiting Edge Features in Graph Neural Networks

    Gong, Liyu / Cheng, Qiang

    2018  

    Abstract: Edge features contain important information about graphs. However, current state-of-the-art neural network models designed for graph learning, e.g. graph convolutional networks (GCN) and graph attention networks (GAT), adequately utilize edge features, ... ...

    Abstract Edge features contain important information about graphs. However, current state-of-the-art neural network models designed for graph learning, e.g. graph convolutional networks (GCN) and graph attention networks (GAT), adequately utilize edge features, especially multi-dimensional edge features. In this paper, we build a new framework for a family of new graph neural network models that can more sufficiently exploit edge features, including those of undirected or multi-dimensional edges. The proposed framework can consolidate current graph neural network models; e.g. graph convolutional networks (GCN) and graph attention networks (GAT). The proposed framework and new models have the following novelties: First, we propose to use doubly stochastic normalization of graph edge features instead of the commonly used row or symmetric normalization approches used in current graph neural networks. Second, we construct new formulas for the operations in each individual layer so that they can handle multi-dimensional edge features. Third, for the proposed new framework, edge features are adaptive across network layers. As a result, our proposed new framework and new models can exploit a rich source of graph information. We apply our new models to graph node classification on several citation networks, whole graph classification, and regression on several molecular datasets. Compared with the current state-of-the-art methods, i.e. GCNs and GAT, our models obtain better performance, which testify to the importance of exploiting edge features in graph neural networks.
    Keywords Computer Science - Machine Learning ; Computer Science - Social and Information Networks ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2018-09-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  3. Article: rFSA: An R Package for Finding Best Subsets and Interactions.

    Lambert, Joshua / Gong, Liyu / Elliott, Corrine F / Thompson, Katherine / Stromberg, Arnold

    The R journal

    2018  Volume 10, Issue 2, Page(s) 295–308

    Abstract: Herein we present the R package rFSA, which implements an algorithm for improved variable selection. The algorithm searches a data space for models of a user-specified form that are statistically optimal under a measure of model quality. Many iterations ... ...

    Abstract Herein we present the R package rFSA, which implements an algorithm for improved variable selection. The algorithm searches a data space for models of a user-specified form that are statistically optimal under a measure of model quality. Many iterations afford a set of
    Language English
    Publishing date 2018-12-08
    Publishing country United States
    Document type Journal Article
    ISSN 2073-4859
    ISSN 2073-4859
    DOI 10.32614/rj-2018-059
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Inter-library loan at ZB MED

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  4. Article: Expression of Cytokines and Chemokines as Predictors of Stroke Outcomes in Acute Ischemic Stroke.

    Martha, Sarah R / Cheng, Qiang / Fraser, Justin F / Gong, Liyu / Collier, Lisa A / Davis, Stephanie M / Lukins, Doug / Alhajeri, Abdulnasser / Grupke, Stephen / Pennypacker, Keith R

    Frontiers in neurology

    2020  Volume 10, Page(s) 1391

    Abstract: Introduction: ...

    Abstract Introduction:
    Language English
    Publishing date 2020-01-15
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2564214-5
    ISSN 1664-2295
    ISSN 1664-2295
    DOI 10.3389/fneur.2019.01391
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Order via subito

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