Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models | Paper Citation Record

Observation 465fba7e-7ace-4604-9c2e-cab308b41a5c · outbound

This paper cites The expression of a tensor or a polyadic as a sum of products,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models The expression of a tensor or a polyadic as a sum of products,

Reference 1

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This paper cites Tensor completion for estimating missing values in visual data,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensor completion for estimating missing values in visual data,

Reference 2

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This paper cites Tensor decompositions for signal processing applica- tions: From two-way to multiway component analysis,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensor decompositions for signal processing applica- tions: From two-way to multiway component analysis,

Reference 3

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This paper cites Tensor decomposition for signal processing and machine learning,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensor decomposition for signal processing and machine learning,

Reference 4

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This paper cites A quadratic penalty method for hypergraph matching,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models A quadratic penalty method for hypergraph matching,

Reference 5

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This paper cites Predictd parallel epigenomics data imputation with cloud-based tensor decomposition,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Predictd parallel epigenomics data imputation with cloud-based tensor decomposition,

Reference 6

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This paper cites Some mathematical notes on three-mode factor analysis,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Some mathematical notes on three-mode factor analysis,

Reference 7

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This paper cites Tensor-train decomposition,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensor-train decomposition,

Reference 8

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This paper cites Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions,

Reference 9

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This paper cites Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives,

Reference 10

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This paper cites Exact tensor completion using t-svd,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Exact tensor completion using t-svd,

Reference 11

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This paper cites Tensor decompositions and applications,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensor decompositions and applications,

Reference 12

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This paper cites Fasthenry: A multipole- accelerated 3-d inductance extraction program,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Fasthenry: A multipole- accelerated 3-d inductance extraction program,

Reference 13

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This paper cites Fastcap: A multipole accelerated 3-d capaci- tance extraction program,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Fastcap: A multipole accelerated 3-d capaci- tance extraction program,

Reference 14

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This paper cites A precorrected-fft method for elec- trostatic analysis of complicated 3-d structures,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models A precorrected-fft method for elec- trostatic analysis of complicated 3-d structures,

Reference 15

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Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Unresolved cited work

Reference 16

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This paper cites Parallel circuit simulation: A historical perspective and recent developments,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Parallel circuit simulation: A historical perspective and recent developments,

Reference 17

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This paper cites A trajectory piecewise-linear approach to model order reduction and fast simulation of nonlinear circuits and micromachined devices,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models A trajectory piecewise-linear approach to model order reduction and fast simulation of nonlinear circuits and micromachined devices,

Reference 18

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This paper cites A multiparameter moment-matching model-reduction approach for gen- erating geometrically parameterized interconnect performance models,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models A multiparameter moment-matching model-reduction approach for gen- erating geometrically parameterized interconnect performance models,

Reference 19

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This paper cites PRIMA: passive reduced- order interconnect macromodeling algorithm,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models PRIMA: passive reduced- order interconnect macromodeling algorithm,

Reference 20

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This paper cites Practical, fast monte carlo statistical static timing analysis: why and how,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Practical, fast monte carlo statistical static timing analysis: why and how,

Reference 21

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This paper cites Stochastic testing method for transistor-level uncertainty quantiﬁcation based on generalized polynomial chaos,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Stochastic testing method for transistor-level uncertainty quantiﬁcation based on generalized polynomial chaos,

Reference 22

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This paper cites Stochastic testing simulator for integrated circuits and MEMS: Hierarchical and sparse techniques,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Stochastic testing simulator for integrated circuits and MEMS: Hierarchical and sparse techniques,

Reference 23

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This paper cites Stochastic modeling of nonlinear circuits via SPICE-compatible spectral equiva- lents,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Stochastic modeling of nonlinear circuits via SPICE-compatible spectral equiva- lents,

Reference 24

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This paper cites Model reduction and simulation of nonlinear circuits via tensor decomposition,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Model reduction and simulation of nonlinear circuits via tensor decomposition,

Reference 25

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This paper cites STA VES: Speedy tensor-aided volterra-based electronic simulator,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models STA VES: Speedy tensor-aided volterra-based electronic simulator,

Reference 26

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This paper cites Tensor train accelerated solution of volume integral equation for 2-d scattering problems and magneto-quasi-static characterization of multiconductor transmission lines,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensor train accelerated solution of volume integral equation for 2-d scattering problems and magneto-quasi-static characterization of multiconductor transmission lines,

Reference 27

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This paper cites The Wiener-Askey polynomial chaos for stochastic differential equations,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models The Wiener-Askey polynomial chaos for stochastic differential equations,

Reference 28

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This paper cites High-order collocation methods for differential equations with random inputs,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models High-order collocation methods for differential equations with random inputs,

Reference 29

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This paper cites Ghanem and P.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Ghanem and P

Reference 30

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This paper cites Stochastic collocation with non- Gaussian correlated parameters via a new quadrature rule,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Stochastic collocation with non- Gaussian correlated parameters via a new quadrature rule,

Reference 31

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This paper cites Stochastic collocation with non-Gaussian cor- related process variations: Theory, algorithms and applications,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Stochastic collocation with non-Gaussian cor- related process variations: Theory, algorithms and applications,

Reference 32

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Observation 652281cd-0f9b-45d3-a8d5-afd889b9be4c · outbound

This paper cites Uncertainty quantiﬁcation of electronic and photonic ICs with non-Gaussian correlated process variations,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Uncertainty quantiﬁcation of electronic and photonic ICs with non-Gaussian correlated process variations,

Reference 33

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This paper cites High-dimensional uncertainty quantiﬁcation of electronic and photonic IC with non-Gaussian correlated process variations,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models High-dimensional uncertainty quantiﬁcation of electronic and photonic IC with non-Gaussian correlated process variations,

Reference 34

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This paper cites A big-data approach to handle process variations: Uncertainty quantiﬁcation by tensor recovery,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models A big-data approach to handle process variations: Uncertainty quantiﬁcation by tensor recovery,

Reference 35

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This paper cites Big-data tensor recovery for high-dimensional uncertainty quan- tiﬁcation of process variations,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Big-data tensor recovery for high-dimensional uncertainty quan- tiﬁcation of process variations,

Reference 36

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This paper cites Enabling high-dimensional hierarchical uncertainty quantiﬁcation by ANOV A and tensor-train decomposition,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Enabling high-dimensional hierarchical uncertainty quantiﬁcation by ANOV A and tensor-train decomposition,

Reference 37

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This paper cites Global sensitivity analysis using low-rank tensor approximations,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Global sensitivity analysis using low-rank tensor approximations,

Reference 38

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This paper cites Reliability analysis of high-dimensional models using low-rank tensor approximations,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Reliability analysis of high-dimensional models using low-rank tensor approximations,

Reference 39

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This paper cites Prediction of multi-dimensional spatial variation data via bayesian tensor completion,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Prediction of multi-dimensional spatial variation data via bayesian tensor completion,

Reference 40

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This paper cites Virtual probe: a statistical framework for low-cost silicon characteriza- tion of nanoscale integrated circuits,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Virtual probe: a statistical framework for low-cost silicon characteriza- tion of nanoscale integrated circuits,

Reference 41

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This paper cites Bayesian cp factorization of incomplete tensors with automatic rank determination,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Bayesian cp factorization of incomplete tensors with automatic rank determination,

Reference 42

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This paper cites Comparing biases for minimal network construction with back-propagation,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Comparing biases for minimal network construction with back-propagation,

Reference 43

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This paper cites Keeping neural networks simple by minimizing the description length of the weights,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Keeping neural networks simple by minimizing the description length of the weights,

Reference 44

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This paper cites Distilling the Knowledge in a Neural Network.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Distilling the Knowledge in a Neural Network

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This paper cites Exploiting linear structure within convolutional networks for efﬁcient evaluation,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Exploiting linear structure within convolutional networks for efﬁcient evaluation,

Reference 46

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This paper cites Tensor rank is np-complete,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensor rank is np-complete,

Reference 47

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This paper cites Speeding-up convolutional neural networks using ﬁne-tuned cp- decomposition,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Speeding-up convolutional neural networks using ﬁne-tuned cp- decomposition,

Reference 48

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This paper cites Ultimate tensorization: compressing convolutional and FC layers alike.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Ultimate tensorization: compressing convolutional and FC layers alike

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This paper cites Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications

Reference 50

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This paper cites MUSCO: Multi-Stage Compression of neural networks.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models MUSCO: Multi-Stage Compression of neural networks

Reference 51

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This paper cites Tensorizing neural networks,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensorizing neural networks,

Reference 52

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This paper cites Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation

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This paper cites Compressing recurrent neural network with tensor train,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Compressing recurrent neural network with tensor train,

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Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensor decomposition for compressing recurrent neural network,

Reference 55

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This paper cites Bayesian Tensorized Neural Networks with Automatic Rank Selection.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Bayesian Tensorized Neural Networks with Automatic Rank Selection

Reference 56

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Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Tensor contraction layers for parsimonious deep nets,

Reference 57

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This paper cites Stein variational gradient descent: A general pur- pose bayesian inference algorithm,.

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models Stein variational gradient descent: A general pur- pose bayesian inference algorithm,

Reference 58

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