Repository logo

Research data supporting "Archetypal landscapes for deep neural networks"

No Thumbnail Available



Change log


Lee, Alpha 


This archive contains input and data files to obtain the results published in:

Archetypal landscapes for deep neural networks
P.C. Verpoort, A.A. Lee, D.J. Wales
Accepted in: Proceedings of the National Academy of Sciences of the USA

The folder data_files contains the training data (and testing data where produced) for the energy landscapes reported in the article. The names of the subfolders correspond to the names given to these datasets in the article.

The LJAT19 dataset was created specifically for this publication, and has not been reported elsewhere. The other two datasets were taken from the UCI Machine Learning Repository and can also be obtained from there; links to the original data source (accessed on April 24th, 2020) are provided in the README files in each subfolder. For completeness and because these data had to be processed to serve as inputs for our landscape analysis software, the data files used for the present work are also contained within this archive.

The folder input_files contains the instruction input files for the GMIN, OPTIM and PATHSAMPLE programs. This is just a starting point, and more manual refinement of parameters and connectivity jobs to run is required in order to fully reproduce the results presented in the article.


Software / Usage instructions

All of the energy-landscapes exploration software used to generate results reported in the publication, along with documentation, examples, and further resources are available for download under the Gnu Public License from the Cambridge Landscape Database.


Machine Learning, Artificial Intelligence, Optimisation, Loss function landscapes


A.A.L. was supported by the Winton Program for the Physics of Sustainability. P.C.V. and D.J.W. were supported by the Engineering and Physical Sciences Research Council