Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model | Paper Citation Record

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This paper cites 9, we anticipate that, between steps 68-1000, points that are closer together in latent space will corre- spond to samples with more similar magnetization.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model 9, we anticipate that, between steps 68-1000, points that are closer together in latent space will corre- spond to samples with more similar magnetization

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This paper cites K¨ unstliche Intelligenz & Gesellschaft: Reflecting Intel- ligent Systems for Diversity, Demography and Democ- racy.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model K¨ unstliche Intelligenz & Gesellschaft: Reflecting Intel- ligent Systems for Diversity, Demography and Democ- racy

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This paper cites Pearson, The London, Edinburgh, and Dublin Philo- sophical Magazine and Journal of Science2, 559 (1901).

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Pearson, The London, Edinburgh, and Dublin Philo- sophical Magazine and Journal of Science2, 559 (1901)

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Cohen, P

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model The information bottleneck method

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This paper cites Alpaydin,Introduction to Machine Learning, 2nd ed., Adaptive Computation and Machine Learning (MIT Press, Cambridge, Mass, 2010).

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Alpaydin,Introduction to Machine Learning, 2nd ed., Adaptive Computation and Machine Learning (MIT Press, Cambridge, Mass, 2010)

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Mohri, A

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Interpretable Machine Learning in Physics: A Review

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This paper cites Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Robbins and S

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Kiefer and J

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Rosenblatt, Psychological Review65, 386 (1958)

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model SGDR: Stochastic Gradient Descent with Warm Restarts

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model The Principles of Deep Learning Theory

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Wang, Phys

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model The critical temperature of the 2D-Ising model through Deep Learning Autoencoders

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Carrasquilla and R

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Baldi and K

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Kunin, J

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Gidel, F

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Refinetti and S

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Goldt, M

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model D’Angelo and L

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Yevick, Eur

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Zinn-Justin,Phase Transitions and Renormalization Group, Oxford Graduate Texts (Oxford University Press, 22 2007)

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Kardar,Statistical Physics of Fields(Cambridge Uni- versity Press, 2007)

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Balian, Y

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Unresolved cited work

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Ising, Z

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Ansel, E

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Weinmann and M

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Cauchy (Cambridge University Press, Cambridge, 2009) pp

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Unresolved cited work

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Jaeger, B

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model On the Expressive Power of Deep Neural Networks

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model As the bottleneck size decreases, more nonlinearities are required to achieve better reconstruction performance

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model This results in a uniform block structure for both the encoder and decoder

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Theoretically, the maximum number of piecewise linear segments in a ReLU model’s function grows exponentially with the depth [52]

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Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model A larger batch size reduces fluctuations in the driving forces across epochs because each batch is more likely to contain similar data points

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This paper cites The function measures the difference between the training batch inputs,x b, and the outputs,ˆ xb.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model The function measures the difference between the training batch inputs,x b, and the outputs,ˆ xb

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:01.766719Z

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Unavailable: canonical work link unavailable.

Observation ec99f3ae-e705-42ed-b183-ef1facfc9444 · outbound

This paper cites an unresolved cited work.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Unresolved cited work

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:01.837271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0a9fc10-a0d7-48ee-80d0-f31c63592d22 · outbound

This paper cites Let’s assume a periodic error signal with period 3, resulting inL MSD k=1 = 1 2 : e=e= [1,− 1 2 ,− 1 2 ,1,− 1 2 ,− 1 2 ,.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Let’s assume a periodic error signal with period 3, resulting inL MSD k=1 = 1 2 : e=e= [1,− 1 2 ,− 1 2 ,1,− 1 2 ,− 1 2 ,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:01.920939Z

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Unavailable: canonical work link unavailable.

Observation 8347c31b-58bc-4601-9acd-3a0722e3699d · outbound

This paper cites 20, 21, and 22.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model 20, 21, and 22

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:02.010451Z

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Observation 219c584b-da00-44f3-a60a-299231c89302 · outbound

This paper cites an unresolved cited work.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:02.090567Z

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Unavailable: canonical work link unavailable.

Observation f7cd3247-a8f8-43cb-b074-fddac06df444 · outbound

This paper cites an unresolved cited work.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:02.256995Z

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Unavailable: canonical work link unavailable.

Observation 136c87e1-bebc-483a-9395-44a0c3487125 · outbound

This paper cites an unresolved cited work.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Unresolved cited work

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:02.424574Z

Source-reported events for the cited work

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Observation 50e77942-4dd3-4781-81df-a6024c558f20 · outbound

This paper cites 26, we explore how the autoencoder forms an approximation for the true energy concept–by looking at the co-evolving output and input distributions of the global energy (Eq.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model 26, we explore how the autoencoder forms an approximation for the true energy concept–by looking at the co-evolving output and input distributions of the global energy (Eq

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:02.572570Z

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Observation ef33ee1a-b8b6-4ee4-820c-ae3793d5ec4e · outbound

This paper cites an unresolved cited work.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Unresolved cited work

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:02.710475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1849b55-402c-46fb-b078-fae0a428d94f · outbound

This paper cites an unresolved cited work.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Unresolved cited work

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:02.842812Z

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Observation c85eb336-2acd-4070-989e-e262fa21d150 · outbound

This paper cites an unresolved cited work.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Unresolved cited work

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:03.020681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc5a14da-e690-484b-8243-d16c84fe13e5 · outbound

This paper cites an unresolved cited work.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:03.204933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 051ddf6c-372e-43a1-bbab-fdfd085d5cd7 · outbound

This paper cites an unresolved cited work.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Unresolved cited work

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-02T07:24:03.373375Z

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