#machinelearing — Public Fediverse posts

#Scientific papers include more and more often #replication packages with valuable #datasets that are then frequently used to train #AI and #MachineLearing models.

But with datasets not properly #documented with information about their #provenance, #biases and other #social concerns, this is risky as the #ML models will use in environments for which the data was not representative, yielding potentially wrong conclusions.

In this work, we have analyzed the datasets in two top dataset journals to study their #documentation #practices and propose a few recommendations to improve the current situation.

Paper accepted in the 𝘕𝘢𝘵𝘶𝘳𝘦'𝘴 𝘚𝘤𝘪𝘦𝘯𝘵𝘪𝘧𝘪𝘤 𝘋𝘢𝘵𝘢 journal

Pre-print https://arxiv.org/pdf/2401.10304

I’ll be at #NeurIPS the coming days. Ping me if you wanna hang out, grab a drink, or talk #probabilistic #machinelearing.

Need to analyze a collection of datasets based on their #PersistenceDiagrams?
Check out our new approach for Principal Geodesic Analysis in the Wasserstein space of #PersistenceDiagrams, with applications to dimension reduction:
https://arxiv.org/abs/2207.10960
#TopologicalDataAnalysis #Visualization #DataScience #MachineLearing #TopologyToolKit
Funded by the European Research Council (ERC) (project TORI, https://erc-tori.github.io/)