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Paper Citation Record · LEDGER

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning

As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:1908.03129.

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

pith.paper-citation-record.v1
1908.03129 v4

Coverage vector

measured 33 of 33 reference resolution

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measured 33 of 33 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation 051df8f0-0e5a-4061-8118-34490dfc0396 · outbound

This paper cites The coming era of precision medicine for intensive care,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning The coming era of precision medicine for intensive care,

Reference 1

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Observation a2e017eb-d7a3-47ef-bbbd-8038eb2f7d97 · outbound

This paper cites Continuous determination of optimal cerebral perfusion pressure in traumatic brain injury*,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Continuous determination of optimal cerebral perfusion pressure in traumatic brain injury*,

Reference 2

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Observation 721c919b-f6b7-47dc-9741-3f0000f6e1fa · outbound

This paper cites Heart rate variability in critical care medicine: a systematic review,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Heart rate variability in critical care medicine: a systematic review,

Reference 3

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Observation 3240e1ef-c17f-4b55-8fa9-f30219db893d · outbound

This paper cites Multifractal analysis of hemodynamic behavior: intraoperative instability and its pharmacological manipulation,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Multifractal analysis of hemodynamic behavior: intraoperative instability and its pharmacological manipulation,

Reference 4

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Observation 417b659e-0861-4197-896e-59ce4afd5848 · outbound

This paper cites Early asymmetric Cardio-Cerebral causality and outcome after severe traumatic brain injury,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Early asymmetric Cardio-Cerebral causality and outcome after severe traumatic brain injury,

Reference 5

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This paper cites Feasibility of individualised severe traumatic brain injury management using an automated assessment of optimal cerebral perfusion pressure: the COGiTATE phase II study protocol,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Feasibility of individualised severe traumatic brain injury management using an automated assessment of optimal cerebral perfusion pressure: the COGiTATE phase II study protocol,

Reference 6

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Observation 3ed01363-adfe-49b1-a195-426630bb9fe2 · outbound

This paper cites Reconstruction of missing physiological signals using artificial neural networks,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Reconstruction of missing physiological signals using artificial neural networks,

Reference 7

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Observation d03ce3e5-605d-4e10-937a-77182425469c · outbound

This paper cites Semi-supervised detection of intracranial pressure alarms using waveform dynamics,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Semi-supervised detection of intracranial pressure alarms using waveform dynamics,

Reference 8

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Observation 8293d0b6-47eb-4f06-b417-cb7142bc4a2a · outbound

This paper cites Alarms in the intensive care unit: how can the number of false alarms be reduced?,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Alarms in the intensive care unit: how can the number of false alarms be reduced?,

Reference 9

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Observation 60cf6ca2-f272-4a4b-aacd-7035c7eb5bad · outbound

This paper cites A signal abnormality index for arterial blood pressure waveforms,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning A signal abnormality index for arterial blood pressure waveforms,

Reference 10

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Observation b9cb3705-747d-46e8-ab02-dbb4b6abcbf9 · outbound

This paper cites An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms,

Reference 11

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Observation 2070e8fc-3f9f-42a1-946f-609a709a6e50 · outbound

This paper cites Anomaly detection: A survey,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Anomaly detection: A survey,

Reference 12

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This paper cites On Calibration of Modern Neural Networks.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning On Calibration of Modern Neural Networks

Reference 13

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This paper cites k-Sparse Autoencoders.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning k-Sparse Autoencoders

Reference 14

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Observation e86c4699-41dd-4a88-8901-0c0a422cd509 · outbound

This paper cites Extracting and composing robust features with denoising autoencoders,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Extracting and composing robust features with denoising autoencoders,

Reference 15

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This paper cites Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,

Reference 16

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Observation ef739eff-fd90-4022-b07e-dd9f1063420f · outbound

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Contractive auto-encoders: Explicit invariance during feature extraction,

Reference 17

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Observation 7dd54483-5f96-4c80-b20c-c8139edee306 · outbound

This paper cites An Introduction to Variational Autoencoders.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning An Introduction to Variational Autoencoders

Reference 18

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Auto-Encoding Variational Bayes

Reference 19

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This paper cites Variational autoencoder based anomaly detection usingreconstruction probability,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Variational autoencoder based anomaly detection usingreconstruction probability,

Reference 20

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Tutorial on Variational Autoencoders

Reference 21

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Unresolved cited work

Reference 22

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Principal components analysis of images via back propagation,

Reference 23

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Chollet and Others, “Keras,” 2015

Reference 24

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 25

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Chollet, Deep Learning with Python

Reference 26

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Ladder Variational Autoencoders

Reference 27

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Density estimation: Variational autoencoders

Reference 28

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning ELBO surgery: yet another way to carve up the variational evidence lower bound,

Reference 29

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This paper cites Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury,.

DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury,

Reference 30

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning b-V AE: Learning basic visual concepts with a constrained variational framework,

Reference 31

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Attenuation in invasive blood pressure measurement systems,

Reference 32

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DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning Tolerance regions for a multivariate normal population,

Reference 33

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Pith citing papers

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