2019-10-23 · preprint · Topic Modeling · en
highly citedClaims drawn from cited facts, not live model generation.
This work, identified by its title, is a preprint whose primary topic is Topic Modeling. As a preprint, it has not yet undergone formal peer review, so it should be treated as preliminary rather than authoritative.
4 cited facts
A small team of three researchers authored this work.
1 cited fact
With 8,346 citations, this work is already an established reference point in its field. It references 0 works, indicating a minimal reliance on prior literature.
3 cited facts
Exploring the Limits of Transfer Learning with a Unified Text-to-Text\n Transformer is a preprint, focusing on Topic Modeling, published in 2019.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text\n Transformer has 8,346 citations in our record, placing it among the highly cited works we track.
Our record lists 3 authors for Exploring the Limits of Transfer Learning with a Unified Text-to-Text\n Transformer. Author names aren't in our data yet — the source link on the page lists the full byline.
Source facts, citations, and refresh stamp for this record.
Sources: openalex_works