English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Tabular classification has traditionally relied on supervised algorithms,which estimate the parameters of a prediction model using its training data.Recently, Prior-Data Fitted Networks (PFNs) such as TabPFN have successfullylearned to classify tabular data in-context: the model parameters are designedto classify new samples based on labelled training samples given after themodel training. While such models show great promise, their applicability toreal-world data remains limited due to the computational scale needed. Here westudy the following question: given a pre-trained PFN for tabular data, what isthe best way to summarize the labelled training samples before feeding them tothe model? We conduct an initial investigation of sketching andfeature-selection methods for TabPFN, and note certain key differences betweenit and conventionally fitted tabular models.

Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks