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

Deep learning has achieved impressive performance in many domains, such ascomputer vision and natural language processing, but its advantage overclassical shallow methods on tabular datasets remains questionable. It isespecially challenging to surpass the performance of tree-like ensembles, suchas XGBoost or Random Forests, on small-sized datasets (less than 1k samples).To tackle this challenge, we introduce HyperTab, a hypernetwork-based approachto solving small sample problems on tabular datasets. By combining theadvantages of Random Forests and neural networks, HyperTab generates anensemble of neural networks, where each target model is specialized to processa specific lower-dimensional view of the data. Since each view plays the roleof data augmentation, we virtually increase the number of training sampleswhile keeping the number of trainable parameters unchanged, which preventsmodel overfitting. We evaluated HyperTab on more than 40 tabular datasets of avarying number of samples and domains of origin, and compared its performancewith shallow and deep learning models representing the currentstate-of-the-art. We show that HyperTab consistently outranks other methods onsmall data (with a statistically significant difference) and scores comparableto them on larger datasets.  We make a python package with the code available to download athttps://pypi.org/project/hypertab/

HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets