Search Results for author: Tongwen Huang

Found 6 papers, 3 papers with code

Finding Similar Exercises in Retrieval Manner

no code implementations15 Mar 2023 Tongwen Huang, Xihua Li, Chao Yi, Xuemin Zhao, Yunbo Cao

When students make a mistake in an exercise, they can consolidate it by ``similar exercises'' which have the same concepts, purposes and methods.

Representation Learning Retrieval

An Empirical Study of Finding Similar Exercises

no code implementations16 Nov 2021 Tongwen Huang, Xihua Li

Education artificial intelligence aims to profit tasks in the education domain such as intelligent test paper generation and consolidation exercises where the main technique behind is how to match the exercises, known as the finding similar exercises(FSE) problem.

Language Modelling Paper generation

BoostingBERT:Integrating Multi-Class Boosting into BERT for NLP Tasks

no code implementations13 Sep 2020 Tongwen Huang, Qingyun She, Junlin Zhang

Our proposed model uses the pre-trained Transformer as the base classifier to choose harder training sets to fine-tune and gains the benefits of both the pre-training language knowledge and boosting ensemble in NLP tasks.

Ensemble Learning Knowledge Distillation

GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction

3 code implementations6 Jul 2020 Tongwen Huang, Qingyun She, Zhiqiang Wang, Junlin Zhang

Inspired by these observations, we propose a novel model named GateNet which introduces either the feature embedding gate or the hidden gate to the embedding layer or hidden layers of DNN CTR models, respectively.

Click-Through Rate Prediction Recommendation Systems

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

31 code implementations23 May 2019 Tongwen Huang, Zhiqi Zhang, Junlin Zhang

In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions.

Click-Through Rate Prediction Feature Importance +1

FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

12 code implementations15 May 2019 Junlin Zhang, Tongwen Huang, Zhiqi Zhang

Although some CTR model such as Attentional Factorization Machine (AFM) has been proposed to model the weight of second order interaction features, we posit the evaluation of feature importance before explicit feature interaction procedure is also important for CTR prediction tasks because the model can learn to selectively highlight the informative features and suppress less useful ones if the task has many input features.

Click-Through Rate Prediction Feature Importance +1

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