no code implementations • 15 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.
no code implementations • 16 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.
no code implementations • 13 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.
3 code implementations • 6 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.
Ranked #20 on Click-Through Rate Prediction on Criteo
31 code implementations • 23 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.
Ranked #18 on Click-Through Rate Prediction on Criteo
12 code implementations • 15 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.
Ranked #17 on Click-Through Rate Prediction on Criteo