Search Results for author: Pengfei Wei

Found 12 papers, 2 papers with code

Graph Domain Adaptation: A Generative View

no code implementations14 Jun 2021 Ruichu Cai, Fengzhu Wu, Zijian Li, Pengfei Wei, Lingling Yi, Kun Zhang

Based on this assumption, we propose a disentanglement-based unsupervised domain adaptation method for the graph-structured data, which applies variational graph auto-encoders to recover these latent variables and disentangles them via three supervised learning modules.

Graph Classification Graph Learning +1

Adaptive Multi-Source Causal Inference

no code implementations31 May 2021 Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang, Tze-Yun Leong

The proposed method can infer causal effects in the target population without prior knowledge of data discrepancy between the additional data sources and the target.

Causal Inference Transfer Learning

Joint Intent Detection and Slot Filling with Wheel-Graph Attention Networks

no code implementations9 Feb 2021 Pengfei Wei, Bi Zeng, Wenxiong Liao

In this paper, we propose a new joint model with a wheel-graph attention network (Wheel-GAT) which is able to model interrelated connections directly for intent detection and slot filling.

Graph Attention Intent Detection +3

Learning Disentangled Semantic Representation for Domain Adaptation

no code implementations22 Dec 2020 Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao

Different from previous efforts on the entangled feature space, we aim to extract the domain invariant semantic information in the latent disentangled semantic representation (DSR) of the data.

Domain Adaptation

Randomized Transferable Machine

no code implementations27 Nov 2020 Pengfei Wei, Tze Yun Leong

Existing studies usually assume that the learned new feature representation is truly \emph{domain-invariant}, and thus directly train a transfer model $\mathcal{M}$ on source domain.

Transfer Learning

Cooperative Heterogeneous Deep Reinforcement Learning

no code implementations NeurIPS 2020 Han Zheng, Pengfei Wei, Jing Jiang, Guodong Long, Qinghua Lu, Chengqi Zhang

Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws.

Continuous Control

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

2 code implementations NeurIPS 2020 Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang

This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks.

imbalanced classification Meta-Learning

Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection

no code implementations8 Aug 2020 Xinyi Xu, Tiancheng Huang, Pengfei Wei, Akshay Narayan, Tze-Yun Leong

This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning (Bengio et al. 2009; Zaremba and Sutskever 2014).

Curriculum Learning Decision Making +3

Subdomain Adaptation with Manifolds Discrepancy Alignment

no code implementations6 May 2020 Pengfei Wei, Yiping Ke, Xinghua Qu, Tze-Yun Leong

Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains.

Transfer Learning

Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy

1 code implementation10 Nov 2019 Xinghua Qu, Zhu Sun, Yew-Soon Ong, Abhishek Gupta, Pengfei Wei

Recent studies have revealed that neural network-based policies can be easily fooled by adversarial examples.

Adversarial Attack Atari Games

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