Paper Citation Record · LEDGER

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.03267.

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pith.paper-citation-record.v1
2506.03267 v1

Coverage vector

measured 39 of 39 reference resolution

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measured 39 of 39 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 18da7a6b-f976-460b-9778-056556c41116 · outbound

This paper cites Axiomatic attribution for deep networks,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Axiomatic attribution for deep networks,

Reference 1

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This paper cites Explanation Space: A New Perspective into Time Series Interpretability.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Explanation Space: A New Perspective into Time Series Interpretability

Reference 2

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This paper cites Explainable ai for time series via virtual inspection layers,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Explainable ai for time series via virtual inspection layers,

Reference 3

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This paper cites ¨Uber den anschaulichen inhalt der quantentheoretischen kinematik und mechanik,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models ¨Uber den anschaulichen inhalt der quantentheoretischen kinematik und mechanik,

Reference 4

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This paper cites The uncertainty principle: a mathematical survey,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models The uncertainty principle: a mathematical survey,

Reference 5

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This paper cites Improving performance of deep learning models with axiomatic attribution priors and expected gradients.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Improving performance of deep learning models with axiomatic attribution priors and expected gradients

Reference 6

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This paper cites Learning important features through propagating activation differences,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Learning important features through propagating activation differences,

Reference 7

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This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 8

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This paper cites A unified approach to interpreting model predictions,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models A unified approach to interpreting model predictions,

Reference 9

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This paper cites Not Just a Black Box: Learning Important Features Through Propagating Activation Differences.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Not Just a Black Box: Learning Important Features Through Propagating Activation Differences

Reference 10

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This paper cites Visualizing and understanding convo- lutional networks,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Visualizing and understanding convo- lutional networks,

Reference 11

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Observation 9aafc6ad-51b8-454b-979a-ab179aed44a9 · outbound

This paper cites Clinical Intervention Prediction and Understanding using Deep Networks.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Clinical Intervention Prediction and Understanding using Deep Networks

Reference 12

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This paper cites Benchmark- ing deep learning interpretability in time series predictions,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Benchmark- ing deep learning interpretability in time series predictions,

Reference 13

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This paper cites Class-specific explainability for deep time series classifiers,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Class-specific explainability for deep time series classifiers,

Reference 14

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Observation 4fd9a3e5-fe00-497a-b05a-3d8d94d45056 · outbound

This paper cites Encoding time-series explanations through self-supervised model behavior consistency,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Encoding time-series explanations through self-supervised model behavior consistency,

Reference 15

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This paper cites Agnostic local ex- planation for time series classification,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Agnostic local ex- planation for time series classification,

Reference 16

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Observation 4df2cf75-2d71-4cd6-a008-4fc289046d45 · outbound

This paper cites Inherently Interpretable Time Series Classification via Multiple Instance Learning.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Inherently Interpretable Time Series Classification via Multiple Instance Learning

Reference 17

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This paper cites Uncertainty principles and signal recovery,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Uncertainty principles and signal recovery,

Reference 18

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On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Entropic uncertainty relations and their applications,

Reference 19

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On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models The uncertainty principle: variations on a theme,

Reference 20

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This paper cites Fourier transforms of functions supported on sets of finite lebesgue measure.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Fourier transforms of functions supported on sets of finite lebesgue measure

Reference 21

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On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models A theorem concerning fourier transforms,

Reference 22

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On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Inequalities in fourier analysis,

Reference 23

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Observation 8c0897cb-bb95-4067-9adc-02e070c95365 · outbound

This paper cites Inceptiontime: Finding alexnet for time series classification,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Inceptiontime: Finding alexnet for time series classification,

Reference 24

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

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 25

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This paper cites A transformer-based framework for multivariate time series representation learning,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models A transformer-based framework for multivariate time series representation learning,

Reference 26

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This paper cites tsai - a state-of-the-art deep learning library for time series and sequential data,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models tsai - a state-of-the-art deep learning library for time series and sequential data,

Reference 27

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This paper cites ” why should i trust you?.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models ” why should i trust you?

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This paper cites Captum: A unified and generic model interpretability library for PyTorch.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Captum: A unified and generic model interpretability library for PyTorch

Reference 29

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On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models The ucr time series archive,

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On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models MIMIC performance dataset,

Reference 31

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On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models US hourly climate dataset,

Reference 32

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On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Dhaka Stock Exchange Historical Data,

Reference 33

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On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models SKForecast Repository,

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On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Monash time series forecasting archive,

Reference 35

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This paper cites Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series

Reference 36

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This paper cites Finding anomalous periodic time series: An application to catalogs of periodic variable stars,.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Finding anomalous periodic time series: An application to catalogs of periodic variable stars,

Reference 37

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This paper cites The Secret Lives of Cepheids: A Multi-Wavelength Study of the Atmospheres and Real-Time Evolution of Classical Cepheids.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models The Secret Lives of Cepheids: A Multi-Wavelength Study of the Atmospheres and Real-Time Evolution of Classical Cepheids

Reference 38

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This paper cites Kallrath, E.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Kallrath, E

Reference 39

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