What is recorded here?
Compared tabular deep architectures under controlled splits/tuning and introduced feature tokenization of numerical and categorical inputs followed by Transformer layers over one object's features.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The comparison deliberately omits several model-agnostic improvements and covers a stated dataset regime. Its conclusion is not universal neural superiority: boosted trees and deep networks lead on different datasets. The simple residual baseline and Transformer adaptation are bundled, not credited with inventing ResNets or attention.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Yury Gorishniy; Ivan Rubachev; Valentin Khrulkov; Artem Babenko
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
FT-Transformer and tuned residual baselines for tabular learning
Compared tabular deep architectures under controlled splits/tuning and introduced feature tokenization of numerical and categorical inputs followed by Transformer layers over one object's features.
Scientific scope
The comparison deliberately omits several model-agnostic improvements and covers a stated dataset regime. Its conclusion is not universal neural superiority: boosted trees and deep networks lead on different datasets. The simple residual baseline and Transformer adaptation are bundled, not credited with inventing ResNets or attention.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Yury Gorishniy; Ivan Rubachev; Valentin Khrulkov; Artem Babenko
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.