Pengfei Liu

RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving

In this work, we propose an efficient and accurate monocular 3D detectio...
01/10/2020 ∙ by Peixuan Li, et al. ∙ 14 ∙ share

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Dynamic Compositional Neural Networks over Tree Structure

Tree-structured neural networks have proven to be effective in learning ...
05/11/2017 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Adversarial Multi-task Learning for Text Classification

Neural network models have shown their promising opportunities for multi...
04/19/2017 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Syntax-based Attention Model for Natural Language Inference

Introducing attentional mechanism in neural network is a powerful concep...
07/22/2016 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Modelling Interaction of Sentence Pair with coupled-LSTMs

Recently, there is rising interest in modelling the interactions of two ...
05/18/2016 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Recurrent Neural Network for Text Classification with Multi-Task Learning

Neural network based methods have obtained great progress on a variety o...
05/17/2016 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Meta Multi-Task Learning for Sequence Modeling

Semantic composition functions have been playing a pivotal role in neura...
02/25/2018 ∙ by Junkun Chen, et al. ∙ 0 ∙ share

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Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network

Effective representation of a text is critical for various natural langu...
08/08/2018 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Meta-Learning Multi-task Communication

In this paper, we describe a general framework: Parameters Read-Write Ne...
10/23/2018 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Contextualized Non-local Neural Networks for Sequence Learning

Recently, a large number of neural mechanisms and models have been propo...
11/21/2018 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Multi-task Learning over Graph Structures

We present two architectures for multi-task learning with neural sequenc...
11/26/2018 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Drug cell line interaction prediction

Understanding the phenotypic drug response on cancer cell lines plays a ...
12/28/2018 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Star-Transformer

Although the fully-connected attention-based model Transformer has achie...
02/25/2019 ∙ by Qipeng Guo, et al. ∙ 0 ∙ share

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TIGS: An Inference Algorithm for Text Infilling with Gradient Search

Text infilling is defined as a task for filling in the missing part of a...
05/26/2019 ∙ by Dayiheng Liu, et al. ∙ 0 ∙ share

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A Combinatorial Algorithm for the Multi-commodity Flow Problem

This paper researches combinatorial algorithms for the multi-commodity f...
04/20/2019 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks

Variants dropout methods have been designed for the fully-connected laye...
07/25/2019 ∙ by Lin Zehui, et al. ∙ 0 ∙ share

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Searching for Effective Neural Extractive Summarization: What Works and What's Next

The recent years have seen remarkable success in the use of deep neural ...
07/08/2019 ∙ by Ming Zhong, et al. ∙ 0 ∙ share

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Zero-shot Text-to-SQL Learning with Auxiliary Task

Recent years have seen great success in the use of neural seq2seq models...
08/29/2019 ∙ by Shuaichen Chang, et al. ∙ 0 ∙ share

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Exploring Domain Shift in Extractive Text Summarization

Although domain shift has been well explored in many NLP applications, i...
08/30/2019 ∙ by Danqing Wang, et al. ∙ 0 ∙ share

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A Closer Look at Data Bias in Neural Extractive Summarization Models

In this paper, we take stock of the current state of summarization datas...
09/30/2019 ∙ by Ming Zhong, et al. ∙ 0 ∙ share

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Learning Sparse Sharing Architectures for Multiple Tasks

Most existing deep multi-task learning models are based on parameter sha...
11/12/2019 ∙ by Tianxiang Sun, et al. ∙ 0 ∙ share

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Multi-Scale Self-Attention for Text Classification

In this paper, we introduce the prior knowledge, multi-scale structure, ...
12/02/2019 ∙ by Qipeng Guo, et al. ∙ 0 ∙ share

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Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study

While neural network-based models have achieved impressive performance o...
01/12/2020 ∙ by Jinlan Fu, et al. ∙ 0 ∙ share

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Pengfei Liu

Featured Co-authors

Zheng Zhang

Xiaodong He

Jian Tang

Qi Zhang

Xipeng Qiu

Xuanjing Huang

Bowen Zhou

Xiangyang Xue

Thomas L. Griffiths

Jie Fu

Jing Huang

RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving

Dynamic Compositional Neural Networks over Tree Structure

Adversarial Multi-task Learning for Text Classification

Syntax-based Attention Model for Natural Language Inference

Modelling Interaction of Sentence Pair with coupled-LSTMs

Recurrent Neural Network for Text Classification with Multi-Task Learning

Meta Multi-Task Learning for Sequence Modeling

Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network

Meta-Learning Multi-task Communication

Contextualized Non-local Neural Networks for Sequence Learning

Multi-task Learning over Graph Structures

Drug cell line interaction prediction

Star-Transformer

TIGS: An Inference Algorithm for Text Infilling with Gradient Search

A Combinatorial Algorithm for the Multi-commodity Flow Problem

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks

Searching for Effective Neural Extractive Summarization: What Works and What's Next

Zero-shot Text-to-SQL Learning with Auxiliary Task

Exploring Domain Shift in Extractive Text Summarization

A Closer Look at Data Bias in Neural Extractive Summarization Models

Learning Sparse Sharing Architectures for Multiple Tasks

Multi-Scale Self-Attention for Text Classification

Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study