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Matrix Shuffle-Exchange Networks for Hard 2D Tasks [article]

Emīls Ozoliņš, Kārlis Freivalds, Agris Šostaks
<span title="2020-10-05">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Its distinct advantage is the capability of retaining full long-range dependency modelling when generalizing to larger instances - much larger than could be processed with models equipped with a dense  ...  We propose a new neural model, called Matrix Shuffle-Exchange network, that can efficiently exploit long-range dependencies in 2D data and has comparable speed to a convolutional neural network.  ...  This research is funded by the Latvian Council of Science, project No. lzp-2018/1-0327.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.15892v2">arXiv:2006.15892v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gtzxxfpdc5hqrpgrijomku266i">fatcat:gtzxxfpdc5hqrpgrijomku266i</a> </span>
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Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces [article]

Philipp Becker, Harit Pandya, Gregor Gebhardt, Cheng Zhao, James Taylor, Gerhard Neumann
<span title="2019-05-17">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
of uncertainty.  ...  unstable matrix inversions.  ...  convolution + transposed convolution output: • Fully Connected 1: 144 ReLU • Transposed Convolution 1: 16, 5 × 5 filter with 4 × 4 stride, ReLU • Transposed Convolution 2: 12, 3 × 3 filter with 4 × 4 stride  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.07357v1">arXiv:1905.07357v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gfv73snzqrb47lm5wkdmyysaou">fatcat:gfv73snzqrb47lm5wkdmyysaou</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191023060745/https://arxiv.org/pdf/1905.07357v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/95/0d/950d2acfd3e329ee667815997ad8cfc914c13f81.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.07357v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images

Wang Xi, Guillaume Devineau, Fabien Moutarde, Jie Yang
<span title="2020-12-03">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/63zsvf7vxzfznojpqgfvpyk2lu" style="color: black;">Algorithms</a> </i> &nbsp;
We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using tree structure skeleton image format.  ...  To the best of our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences.  ...  without tree traversal order representation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/a13120319">doi:10.3390/a13120319</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wcj46asfanetpdm5h3e5bfp3hm">fatcat:wcj46asfanetpdm5h3e5bfp3hm</a> </span>
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Fast Discrete Polynomial Transforms with Applications to Data Analysis for Distance Transitive Graphs

J. R. Driscoll, D. M. Healy, D. N. Rockmore
<span title="">1997</span> <i title="Society for Industrial &amp; Applied Mathematics (SIAM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7dys7zoberdktmxyjciuy5bnse" style="color: black;">SIAM journal on computing (Print)</a> </i> &nbsp;
1 g denote a set of polynomials with complex coe cients. Let Z = fz 0 ; : : : ; z n?1 g C denote any set of sample points. For any f = (f 0 ; : : : ; f n?  ...  1 ) 2 C n the discrete polynomial transform of f (with respect to P and Z) is de ned as the collection  ...  This has the form of polynomial evaluation, or multiplication by the transpose of the generalized Vandermonde matrix associated with the polynomials p k and the evaluation points i : 0 B B B @ b f( 0 )  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1137/s0097539792240121">doi:10.1137/s0097539792240121</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5c2gds4ni5fljpvwebav5cgaja">fatcat:5c2gds4ni5fljpvwebav5cgaja</a> </span>
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Book announcements

<span title="">1995</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lx7dev2le5anbg6oarljwh7lie" style="color: black;">Discrete Applied Mathematics</a> </i> &nbsp;
Graphical representation. Assumptions made in modelling component behaviour. Chapter 10: Electrical Networks: Matrix Equations. Kirchhoff's voltage law equations. Fundamental cycles.  ...  Comparison with functions. The converse of a relation. The composition of two relations. Graphs of relations. Relation-matrices M(R). General matrices. Matrix multiplication.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/0166-218x(95)90033-c">doi:10.1016/0166-218x(95)90033-c</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3mrk72xg4fb6biuzhbn5b7a2o4">fatcat:3mrk72xg4fb6biuzhbn5b7a2o4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171003130921/http://publisher-connector.core.ac.uk/resourcesync/data/elsevier/pdf/cc1/aHR0cDovL2FwaS5lbHNldmllci5jb20vY29udGVudC9hcnRpY2xlL3BpaS8wMTY2MjE4eDk1OTAwMzNj.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/83/c3/83c3f37f9e48283d03c8cbb264fbda7b44e5edbc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/0166-218x(95)90033-c"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Manifold Regularized Slow Feature Analysis for Dynamic Texture Recognition [article]

Jie Miao, Xiangmin Xu, Xiaofen Xing, Dacheng Tao
<span title="2017-06-09">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
temporal transition and retaining the locality of their variations.  ...  MR-SFA for dynamic texture recognition is proposed in the following steps: 1) learning feature extraction functions as convolution filters by MR-SFA, 2) extracting local features by convolution and pooling  ...  All of the vectors in the paper are column vectors. The matrix and vector transpose is denoted by the superscript T. For example, X T is the transpose of X. A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1706.03015v1">arXiv:1706.03015v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/el2ljsuv5vfz7eotxt34xmagde">fatcat:el2ljsuv5vfz7eotxt34xmagde</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200917190725/https://arxiv.org/pdf/1706.03015v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fe/ac/feacb621d8f465a6433c54f5e1ddb9a8fbc2203a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1706.03015v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Graph convolutional networks: a comprehensive review

Si Zhang, Hanghang Tong, Jiejun Xu, Ross Maciejewski
<span title="2019-11-10">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/57o5y4hm2rgexgjtw745w52asu" style="color: black;">Computational Social Networks</a> </i> &nbsp;
First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details.  ...  Then, we categorize different graph convolutional networks according to the areas of their applications.  ...  The content of the information in this document does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. The U.S.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40649-019-0069-y">doi:10.1186/s40649-019-0069-y</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/usvlugxj6jcrzesm7dthrecp3m">fatcat:usvlugxj6jcrzesm7dthrecp3m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191212204213/https://link.springer.com/content/pdf/10.1186/s40649-019-0069-y.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d4/74/d474bf3cac3f3824778dbc494bd2e89f6f8e57dc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40649-019-0069-y"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Deconvolutional Paragraph Representation Learning [article]

Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao, Lawrence Carin
<span title="2017-09-22">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, the quality of sentences during RNN-based decoding (reconstruction) decreases with the length of the text.  ...  We propose a sequence-to-sequence, purely convolutional and deconvolutional autoencoding framework that is free of the above issue, while also being computationally efficient.  ...  Deconvolutional decoder We apply the deconvolution with stride (i.e., convolutional transpose), as the conjugate operation of convolution, to decode the latent representation, h, back to the source (discrete  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1708.04729v3">arXiv:1708.04729v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nn32yofpybcuncjz2uqye3youq">fatcat:nn32yofpybcuncjz2uqye3youq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200826065031/https://arxiv.org/pdf/1708.04729v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a2/b2/a2b2e4293deb2dca81c756cdb3cff2d9eb998d7e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1708.04729v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Card-Shuffling via Convolutions of Projections on Combinatorial Hopf Algebras [article]

C. Y. Amy Pang
<span title="2015-03-28">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The present note replaces in this construction the coproduct-then-product map with convolutions of projections to the graded subspaces, effectively allowing us to dictate the distribution of sizes of the  ...  An important example is removing one "vertex" and reattaching it, in analogy with top-to-random shuffling. This larger family of Markov chains all admit analysis by Hopf-algebraic techniques.  ...  In other words, the transition matrix of the riffle-shuffling of n cards is 1 2 n m∆ T Bn , the transpose of the matrix of the linear operator 1 2 n m∆ with respect to the basis B n of words.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1503.08368v1">arXiv:1503.08368v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vomrizzxavftjp63ezee2ha34y">fatcat:vomrizzxavftjp63ezee2ha34y</a> </span>
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Two-dimensional wreath product group-based image processing

Richard Foote, Gagan Mirchandani, Daniel Rockmore
<span title="">2004</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ezljl2d3lzga5efenbxdvvfcpa" style="color: black;">Journal of symbolic computation</a> </i> &nbsp;
Conditions for separability of these transforms are established.  ...  A theoretical foundation to the notion of 2D transform and 2D signal processing is given, focusing on 2D group-based transforms, of which the 2D Haar and 2D Fourier transforms are particular instances.  ...  In each family the WPC group Z (n) transitively permutes the set X of leaves of the tree (i.e. the nodes at level n).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jsc.2002.06.004">doi:10.1016/j.jsc.2002.06.004</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pfjgbs5vrnhqtcpsyeksg3uzqe">fatcat:pfjgbs5vrnhqtcpsyeksg3uzqe</a> </span>
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A Comprehensive Survey on Graph Neural Networks [article]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
<span title="2019-12-04">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects.  ...  We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and  ...  E The set of edges in a graph. e ij An edge e ij ∈ E. N (v) The neighbors of a node v. A The graph adjacency matrix. A T The transpose of the matrix A. A n , n ∈ Z The n th power of A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.00596v4">arXiv:1901.00596v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xxuchvawonhczay2sgjgzw5wgu">fatcat:xxuchvawonhczay2sgjgzw5wgu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930031957/https://arxiv.org/pdf/1901.00596v4.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e4/a3/e4a37759d8673e32f15e43894a0cbfbf07a18036.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.00596v4" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Urban Landscape Information Construction and Visual Communication Design Based on Digital Image Matrix Reconstruction

Jie Yu, Liping Zhang, Ning Cao
<span title="2022-06-06">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wpareqynwbgqdfodcyhh36aqaq" style="color: black;">Mathematical Problems in Engineering</a> </i> &nbsp;
The main idea of the algorithm is to structure the iteration matrix at each step, that is, to reassign the elements on each diagonal of the matrix by the operator.  ...  With the support of the Oculus SDK, the secondary rendering of the overall 3D cityscape in the immersive virtual reality module is carried out, and the corresponding OSG camera browsing interface is constructed  ...  Conflicts of Interest e authors declare that they have no conflicts of interest or personal relationships that could have appeared to influence the work reported in this paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/8517464">doi:10.1155/2022/8517464</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5grfvorb2ja75i6mo2l6o32rk4">fatcat:5grfvorb2ja75i6mo2l6o32rk4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220611144039/https://downloads.hindawi.com/journals/mpe/2022/8517464.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/dc/c7/dcc7499a5bcacd6f7986984be6df62c4f06493be.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/8517464"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Card-Shuffling via Convolutions of Projections on Combinatorial Hopf Algebras

C. Y. Amy Pang
<span title="2015-01-01">2015</span> <i title="Centre pour la Communication Scientifique Directe (CCSD)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/aagtqr2vajamvduhte7kigeygi" style="color: black;">Discrete Mathematics &amp; Theoretical Computer Science</a> </i> &nbsp;
The present note replaces in this construction the coproduct-then-product map with convolutions of projections to the graded subspaces, effectively allowing us to dictate the distribution of sizes of the  ...  An important example is removing one "vertex" and reattaching it, in analogy with top-to-random shuffling. This larger family of Markov chains all admit analysis by Hopf-algebraic techniques.  ...  :i) for all w, z ∈ B, the expansion of m(w ⊗ z) in the B basis has all coefficients non-negative; T Bn , the transpose of the matrix of the linear operator 1 2 n m∆ with respect to the basis B n of words  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.46298/dmtcs.2511">doi:10.46298/dmtcs.2511</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vyhxz7ac7new5cmgpka2feecii">fatcat:vyhxz7ac7new5cmgpka2feecii</a> </span>
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Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 [article]

Li Yi, Lin Shao, Manolis Savva, Haibin Huang, Yang Zhou, Qirui Wang, Benjamin Graham, Martin Engelcke, Roman Klokov, Victor Lempitsky, Yuan Gan, Pengyu Wang, Kun Liu, Fenggen Yu (+32 others)
<span title="2017-10-27">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The benchmark consists of two tasks: part-level segmentation of 3D shapes and 3D reconstruction from single view images.  ...  A few novel deep learning architectures have been proposed on various 3D representations on both tasks. We report the techniques used by each team and the corresponding performances.  ...  This representation is obtained by constructing a k-d tree for the point clouds. The output is fed into a fully-convolutional network with skip connections.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1710.06104v2">arXiv:1710.06104v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nczmtumuj5g27dag5vziyuf47a">fatcat:nczmtumuj5g27dag5vziyuf47a</a> </span>
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Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions [article]

Konstantinos Kogkalidis, Michael Moortgat
<span title="2022-04-21">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
categories, with significant implications for grammars previously deemed too complex to find practical use.  ...  In this work, we revisit constructive supertagging from a graph-theoretic perspective, and propose a framework based on heterogeneous dynamic graph convolutions aimed at exploiting the distinctive structure  ...  Tree nodes are represented as vectors of dimensionality d n , the totality of n nodes in the graph then being a matrix N ∈ R n×dn ; tree edges are converted into a sparse connectivity matrix E n ∈ N s×  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.12235v2">arXiv:2203.12235v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jjpzb3owl5gtxdadkz2c6i556i">fatcat:jjpzb3owl5gtxdadkz2c6i556i</a> </span>
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