Paper Citation Record · LEDGER

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures

As of 16 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:1908.04030.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.04030 v4

Coverage vector

measured 44 of 44 reference resolution

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measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 1c391567-507b-4a76-a559-9a5af465c8b6 · outbound

This paper cites In: Proceedings of the IEEE conference on computer vision and pattern recognition.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 1

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This paper cites Expert Systems with Applications 36(3), 5932– 5941 (2009).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Expert Systems with Applications 36(3), 5932– 5941 (2009)

Reference 2

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This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 3

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This paper cites John Wiley & Sons, Inc., New York, NY, USA (2002).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures John Wiley & Sons, Inc., New York, NY, USA (2002)

Reference 4

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This paper cites In: 2018 24th International Conference on Pattern Recognition (ICPR).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: 2018 24th International Conference on Pattern Recognition (ICPR)

Reference 5

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This paper cites best of many.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures best of many

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This paper cites Oxford university press (1995).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Oxford university press (1995)

Reference 7

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This paper cites Springer-Verlag New York, Inc., Secaucus, NJ, USA (2006).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Springer-Verlag New York, Inc., Secaucus, NJ, USA (2006)

Reference 8

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This paper cites Weight Uncertainty in Neural Networks.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Weight Uncertainty in Neural Networks

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This paper cites bioRxiv (2015).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures bioRxiv (2015)

Reference 10

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Observation a060d890-4494-4958-a96b-db1a1866db74 · outbound

This paper cites Space Weather 15(8), 1004–1019 (2017).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Space Weather 15(8), 1004–1019 (2017)

Reference 11

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Observation 26eb0b7f-1007-4762-9926-6fb63a16a4bf · outbound

This paper cites In: Artiﬁcial Intelligence and Statistics.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: Artiﬁcial Intelligence and Statistics

Reference 12

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This paper cites In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

Reference 13

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This paper cites Data Mining and Knowledge Discovery 33(4), 917–963 (2019).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Data Mining and Knowledge Discovery 33(4), 917–963 (2019)

Reference 14

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This paper cites Bayesian Recurrent Neural Networks.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Bayesian Recurrent Neural Networks

Reference 15

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This paper cites In: international conference on machine learn- ing.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: international conference on machine learn- ing

Reference 16

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This paper cites In: Advances in neural information processing systems.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: Advances in neural information processing systems

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This paper cites Generating Sequences With Recurrent Neural Networks.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Generating Sequences With Recurrent Neural Networks

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This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 19

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures World Models

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This paper cites Springer Science & Business Media (2006).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Springer Science & Business Media (2006)

Reference 21

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This paper cites Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets

Reference 22

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: International Conference on Machine Learning

Reference 23

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This paper cites In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC)

Reference 24

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: 2nd International Conference on Learning Representations, ICLR 2014, Banﬀ, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014), http://arxiv.org/abs/1312

Reference 25

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Observation 60c1965d-2424-4860-8a46-52bceb4a2df8 · outbound

This paper cites Pattern Recognition Letters 42, 11–24 (2014).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Pattern Recognition Letters 42, 11–24 (2014)

Reference 26

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures ROBOMECH journal 1(1), 1 (2014)

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This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 28

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This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Recurrent Gaussian Processes

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures John Wiley & Sons (2015)

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Unresolved cited work

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Technical University of Denmark 7(15), 510 (2008)

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: Summer School on Machine Learning

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This paper cites Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1984), 20110550 (2013).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1984), 20110550 (2013)

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: European conference on computer vision

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This paper cites Human Motion Trajectory Prediction: A Survey.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Human Motion Trajectory Prediction: A Survey

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)

Reference 38

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This paper cites Institute of Math- ematical Statistics Textbooks, Cambridge University Press (2013).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures Institute of Math- ematical Statistics Textbooks, Cambridge University Press (2013)

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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: 2018 IEEE International Conference on Robotics and Automation (ICRA)

Reference 40

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This paper cites In: Advances in neural information processing sys- tems.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: Advances in neural information processing sys- tems

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This paper cites International Journal of Computer Science & Engineering Technology 2(3), 71–83 (2011).

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures International Journal of Computer Science & Engineering Technology 2(3), 71–83 (2011)

Reference 42

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This paper cites In: Advances in neural information processing systems.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: Advances in neural information processing systems

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This paper cites In: Advances in neural information processing systems.

Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures In: Advances in neural information processing systems

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Pith citing papers

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