Methods › General › Deep Tabular Learning › NON

Network On Network

NON

389 papers tagged archive 2025-07-28

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

Network On Network (NON) is practical tabular data classification model based on deep neural network to provide accurate predictions. Various deep methods have been proposed and promising progress has been made. However, most of them use operations like neural network and factorization machines to fuse the embeddings of different features directly, and linearly combine the outputs of those operations to get the final prediction. As a result, the intra-field information and the non-linear interactions between those operations (e.g. neural network and factorization machines) are ignored. Intra-field information is the information that features inside each field belong to the same field. NON is proposed to take full advantage of intra-field information and non-linear interactions. It consists of three components: field-wise network at the bottom to capture the intra-field information, across field network in the middle to choose suitable operations data-drivenly, and operation fusion network on the top to fuse outputs of the chosen operations deeply

Source: Network On Network for Tabular Data Classification in...

Papers archive 2025-07-28

30 shown of 389, 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.

Tasks archive 2025-07-28

20 shown of 280 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Federated Learning17
Language Modelling11
Time Series10
regression10
reinforcement-learning10
Clustering8
Decision Making8
Denoising8
Reinforcement Learning (RL)8
Retrieval8
Classification7
Fairness7
Language Modeling7
Management7
Object Detection7
Reinforcement Learning7
object-detection7
Binary Classification6
Deep Learning6
Semantic Segmentation6

Usage over time archive 2025-07-28

Papers per year tagged with NON: 2020 to 2025, peak 111 111 0 2020: 2 papers 2020 2021: 22 papers 2021 2022: 104 papers 2022 2023: 111 papers 2023 2024: 109 papers 2024 2025: 41 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (389 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

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