Search Results for author: Tongwen Huang

Found 4 papers, 3 papers with code

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

1 code implementation6 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

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

3 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

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

1 code implementation15 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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