Methods › General › Deep Tabular Learning › DCN-V2

DCN-V2

3 papers tagged archive 2025-07-28

Introduced by Ruoxi Wang et al. in DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

DCN-V2 is an architecture for learning-to-rank that improves upon the original DCN model. It first learns explicit feature interactions of the inputs (typically the embedding layer) through cross layers, and then combines with a deep network to learn complementary implicit interactions. The core of DCN-V2 is the cross layers, which inherit the simple structure of the cross network from DCN, however it is significantly more expressive at learning explicit and bounded-degree cross features.

Paper Source

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style
16 Feb 2024 · 1 repository · arXiv:2402.10381
Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models
14 Nov 2021 · 0 repositories · arXiv:2111.07355
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
19 Aug 2020 · 12 repositories · arXiv:2008.13535

Syntology ran 2 of 14 samples · 12 unverified

Tasks archive 2025-07-28

12 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

Task	Papers
Recommendation Systems	2
Click-Through Rate Prediction	1
Ensemble Learning	1
Fracture detection	1
Image Generation	1
Learning-To-Rank	1
Medical Object Detection	1
Multi-modal Recommendation	1
Object Detection	1
Sentence	1
Transfer Learning	1
object-detection	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Deep Tabular Learning Learning to Rank Models

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