Search Results for author: Dongkuan Xu

Found 8 papers, 2 papers with code

Data Augmentation with Adversarial Training for Cross-Lingual NLI

no code implementations ACL 2021 Xin Dong, Yaxin Zhu, Zuohui Fu, Dongkuan Xu, Gerard de Melo

Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a cross-lingual model that can then be applied to multiple new languages.

Cross-Lingual Natural Language Inference Data Augmentation

Rethinking Network Pruning -- under the Pre-train and Fine-tune Paradigm

no code implementations NAACL 2021 Dongkuan Xu, Ian E. H. Yen, Jinxi Zhao, Zhibin Xiao

In particular, common wisdom in pruning CNN states that sparse pruning technique compresses a model more than that obtained by reducing number of channels and layers (Elsen et al., 2020; Zhu and Gupta, 2017), while existing works on sparse pruning of BERT yields inferior results than its small-dense counterparts such as TinyBERT (Jiao et al., 2020).

Network Pruning

Parameterized Explainer for Graph Neural Network

2 code implementations NeurIPS 2020 Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang

The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to a lack of generalizability and hindering it from being used in the inductive setting.

Graph Classification

Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification

no code implementations29 Jul 2020 Xin Dong, Yaxin Zhu, Yupeng Zhang, Zuohui Fu, Dongkuan Xu, Sen yang, Gerard de Melo

The resulting model then serves as a teacher to induce labels for unlabeled target language samples that can be used during further adversarial training, allowing us to gradually adapt our model to the target language.

Classification General Classification +2

Longitudinal Deep Kernel Gaussian Process Regression

no code implementations24 May 2020 Junjie Liang, Yanting Wu, Dongkuan Xu, Vasant Honavar

Specifically, L-DKGPR eliminates the need for ad hoc heuristics or trial and error using a novel adaptation of deep kernel learning that combines the expressive power of deep neural networks with the flexibility of non-parametric kernel methods.

Gaussian Processes Variational Inference

How Do We Move: Modeling Human Movement with System Dynamics

no code implementations1 Mar 2020 Hua Wei, Dongkuan Xu, Junjie Liang, Zhenhui Li

To the best of our knowledge, we are the first to learn to model the state transition of moving agents with system dynamics.

Imitation Learning

LMLFM: Longitudinal Multi-Level Factorization Machine

1 code implementation11 Nov 2019 Junjie Liang, Dongkuan Xu, Yiwei Sun, Vasant Honavar

However, the current state-of-the-art methods are unable to select the most predictive fixed effects and random effects from a large number of variables, while accounting for complex correlation structure in the data and non-linear interactions among the variables.

Variable Selection

PIGMIL: Positive Instance Detection via Graph Updating for Multiple Instance Learning

no code implementations12 Dec 2016 Dongkuan Xu, Jia Wu, Wei zhang, Yingjie Tian

To the end, we propose a positive instance detection via graph updating for multiple instance learning, called PIGMIL, to detect TPI accurately.

Multiple Instance Learning

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