Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation | Paper Citation Record

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This paper cites u rek, Afra Feyza Aky \.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation u rek, Afra Feyza Aky \

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This paper cites Good-enough compositional data augmentation.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Good-enough compositional data augmentation

Reference 2

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This paper cites Bolker, and Steven C.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Bolker, and Steven C

Reference 3

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This paper cites Domain adaptation - can quantity compensate for quality? Annals of Mathematics and Artificial Intelligence, 70: 0 185--202, 2014.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Domain adaptation - can quantity compensate for quality? Annals of Mathematics and Artificial Intelligence, 70: 0 185--202, 2014

Reference 4

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This paper cites Analysis of representations for domain adaptation.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Analysis of representations for domain adaptation

Reference 5

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This paper cites A theory of learning from different domains.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation A theory of learning from different domains

Reference 6

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Impossibility theorems for domain adaptation

Reference 7

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This paper cites Representation learning: A review and new perspectives.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Representation learning: A review and new perspectives

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This paper cites A survey of mix-based data augmentation: Taxonomy, methods, applications, and explainability.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation A survey of mix-based data augmentation: Taxonomy, methods, applications, and explainability

Reference 9

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This paper cites Syntactic Structures.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Syntactic Structures

Reference 10

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation On certain formal properties of grammars

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Mixed Models: Theory and Applications with R

Reference 12

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This paper cites Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

Reference 13

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Unresolved cited work

Reference 14

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This paper cites Reddy, and Vignesh Subbian.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Reddy, and Vignesh Subbian

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This paper cites Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Unresolved cited work

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This paper cites Higgins, Lo \"i c Matthey, A.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Higgins, Lo \"i c Matthey, A

Reference 18

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This paper cites Set functions for time series.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Set functions for time series

Reference 19

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This paper cites Towards equipping transformer with the ability of systematic compositionality.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Towards equipping transformer with the ability of systematic compositionality

Reference 20

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Unresolved cited work

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Johnson, Tom J

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Measuring compositional generalization: A comprehensive method on realistic data

Reference 23

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation COGS : A compositional generalization challenge based on semantic interpretation

Reference 24

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This paper cites Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks

Reference 25

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Lake, Tomer D

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation An Introduction to Symbolic Dynamics and Coding

Reference 27

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Unresolved cited work

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Challenging common assumptions in the unsupervised learning of disentangled representations

Reference 29

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Learning representations for time series clustering

Reference 30

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Universal grammar

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Lost in latent space: Examining failures of disentangled models at combinatorial generalisation

Reference 32

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation The role of disentanglement in generalisation

Reference 33

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Unresolved cited work

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This paper cites Revisiting the compositional generalization abilities of neural sequence models.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Revisiting the compositional generalization abilities of neural sequence models

Reference 35

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Two-neutron knockout as a probe of the composition of states in $^{22}$Mg, $^{23}$Al, and $^{24}$Si

Reference 36

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Scikit-learn: Machine learning in python

Reference 37

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Towards Generating Real-World Time Series Data

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Pollard, Alistair E

Reference 39

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Improving compositional generalization with latent structure and data augmentation

Reference 40

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science

Reference 41

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Compositional generalization by factorizing alignment and translation

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Transformer grammars: Augmenting transformer language models with syntactic inductive biases at scale

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Motiflets: Simple and accurate detection of motifs in time series

Reference 44

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation u gelgen, Frederik Tr \

Reference 45

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Early Prediction of Causes (not Effects) in Healthcare by Long-Term Clinical Time Series Forecasting

Reference 46

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Compositionality

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Unresolved cited work

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 49

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Vincent, R

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Time series data augmentation for deep learning: A survey

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Compositional generalization from first principles

Reference 52

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Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Introduction to symbolic dynamics

Reference 53

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This paper cites Compositional generalization in unsupervised compositional representation learning: a study on disentanglement and emergent language.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Compositional generalization in unsupervised compositional representation learning: a study on disentanglement and emergent language

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:03:16.089469Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c3d135a6-bd8e-41eb-b9e0-28fe00e41a92 · outbound

This paper cites Robust Augmentation for Multivariate Time Series Classification.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Robust Augmentation for Multivariate Time Series Classification

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:03:11.212137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f3cabd3f-a1ea-4b2d-a9c8-e0bbede9f962 · outbound

This paper cites Time-series generative adversarial networks.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Time-series generative adversarial networks

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:03:15.810570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 786ec63d-d13f-43cd-ac63-0f93bcecc132 · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T15:03:10.743835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4d57b36-e0f0-4f02-93fb-f88cb5b1f5cd · outbound

This paper cites write newline.

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation write newline

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T15:03:10.890699Z

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