Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

DOI

This dataset contains all relevant data to reproduce the results of the paper titled "Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model" and supplementary figures.

"code.tar.xz" contains the code to generate the datasets and model checkpoints used in the analysis. Information on the code including instructions on how to reproduce the data can be found in the file README.md.

"ising_data.tar.xz" contains the resulting datasets. The datasets where generated with Markov-Chain-Monte-Carlo based on Glauber Dynamics of the Ising model and used for training and validation.

"checkpoints.tar.xz" contains the resulting model checkpoints, including the used training configuration parameters and some metrics computed based on the datasets included in "ising_data.tar.xz". The model checkpoints where saved during training with different hyper-parameters.

"figures.tar.xz" contains supplementary figures for the paper created based on the model checkpoints/metrics in "checkpoints.tar.xz" using the data from "ising_data.tar.xz". These contain figures for all the different hyperparameters and datasets which are not shown in the paper.

Identifier
DOI https://doi.org/10.18419/DARUS-6128
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-6128
Provenance
Creator Weinmann, Max ORCID logo; Klopotek, Miriam ORCID logo
Publisher DaRUS
Contributor Weinmann, Max; Klopotek, Miriam
Publication Year 2026
Funding Reference Baden-Württemberg Ministry of Science, Research and Arts Az. 33-7533-9-19/54/5 ; DFG EXC 2075 - 390740016
Rights info:eu-repo/semantics/openAccess
OpenAccess true
Contact Weinmann, Max (University of Stuttgart); Klopotek, Miriam (University of Stuttgart)
Representation
Resource Type Dataset
Format application/x-xz
Size 553915717896; 136564; 290231264; 22157152
Version 1.0
Discipline Computer Science; Computer Science, Electrical and System Engineering; Engineering Sciences; Natural Sciences; Physics