Liyuan Liu | DeepAI

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Yu Zhang
406 publications
Jianfeng Gao
241 publications
Chao Zhang
239 publications
Fei Wu
174 publications
Jiawei Han
140 publications
Tao Chen
134 publications
Heng Ji
126 publications
Xiang Ren
120 publications
Qi Li
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Shiliang Pu
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Weizhu Chen
89 publications

∙ 06/19/2023

Parallel Data Compression Techniques

With endless amounts of data and very limited bandwidth, fast data compr...

0 David Noel, et al. ∙

∙ 04/17/2023

Bridging Discrete and Backpropagation: Straight-Through and Beyond

Backpropagation, the cornerstone of deep learning, is limited to computi...

0 Liyuan Liu, et al. ∙

∙ 06/14/2022

SoTeacher: A Student-oriented Teacher Network Training Framework for Knowledge Distillation

How to train an ideal teacher for knowledge distillation is still an ope...

0 Chengyu Dong, et al. ∙

∙ 02/15/2022

PILED: An Identify-and-Localize Framework for Few-Shot Event Detection

Practical applications of event extraction systems have long been hinder...

0 Sha Li, et al. ∙

∙ 10/07/2021

Double Descent in Adversarial Training: An Implicit Label Noise Perspective

Here, we show that the robust overfitting shall be viewed as the early p...

0 Chengyu Dong, et al. ∙

∙ 06/21/2021

Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training

With recent advances in distantly supervised (DS) relation extraction (R...

12 Tao Chen, et al. ∙

∙ 06/17/2021

Multi-head or Single-head? An Empirical Comparison for Transformer Training

Multi-head attention plays a crucial role in the recent success of Trans...

0 Liyuan Liu, et al. ∙

∙ 05/28/2021

UCPhrase: Unsupervised Context-aware Quality Phrase Tagging

Identifying and understanding quality phrases from context is a fundamen...

0 Xiaotao Gu, et al. ∙

∙ 02/15/2021

Data Profiling for Adversarial Training: On the Ruin of Problematic Data

Multiple intriguing problems hover in adversarial training, including ro...

0 Chengyu Dong, et al. ∙

∙ 10/15/2020

Overfitting or Underfitting? Understand Robustness Drop in Adversarial Training

Our goal is to understand why the robustness drops after conducting adve...

0 Zichao Li, et al. ∙

∙ 08/18/2020

Very Deep Transformers for Neural Machine Translation

We explore the application of very deep Transformer models for Neural Ma...

0 Xiaodong Liu, et al. ∙

∙ 05/01/2020

Partially-Typed NER Datasets Integration: Connecting Practice to Theory

While typical named entity recognition (NER) models require the training...

5 Shi Zhi, et al. ∙

∙ 04/17/2020

Understanding the Difficulty of Training Transformers

Transformers have been proved effective for many deep learning tasks. Tr...

0 Liyuan Liu, et al. ∙

∙ 10/09/2019

Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling

Sequence labeling is a fundamental framework for various natural languag...

0 Ouyu Lan, et al. ∙

∙ 09/03/2019

CrossWeigh: Training Named Entity Tagger from Imperfect Annotations

Everyone makes mistakes. So do human annotators when curating labels for...

0 Zihan Wang, et al. ∙

∙ 08/27/2019

Facet-Aware Evaluation for Extractive Text Summarization

Commonly adopted metrics for extractive text summarization like ROUGE fo...

0 Yuning Mao, et al. ∙

∙ 08/14/2019

Raw-to-End Name Entity Recognition in Social Media

Taking word sequences as the input, typical named entity recognition (NE...

0 Liyuan Liu, et al. ∙

∙ 08/08/2019

On the Variance of the Adaptive Learning Rate and Beyond

The learning rate warmup heuristic achieves remarkable success in stabil...

1 Liyuan Liu, et al. ∙

∙ 04/19/2019

Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction

In recent years there is surge of interest in applying distant supervisi...

0 Maosen Zhang, et al. ∙

∙ 12/27/2018

Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label ins...

0 Yujin Yuan, et al. ∙

∙ 09/10/2018

Learning Named Entity Tagger using Domain-Specific Dictionary

Recent advances in deep neural models allow us to build reliable named e...

0 Jingbo Shang, et al. ∙

∙ 04/20/2018

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling

Many efforts have been made to facilitate natural language processing ta...

0 Liyuan Liu, et al. ∙

∙ 03/09/2018

Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings

Expert finding is an important task in both industry and academia. It is...

0 Huan Gui, et al. ∙

∙ 12/19/2017

Wikidata Vandalism Detection - The Loganberry Vandalism Detector at WSDM Cup 2017

Wikidata is the new, large-scale knowledge base of the Wikimedia Foundat...

0 Qi Zhu, et al. ∙

∙ 09/13/2017

Empower Sequence Labeling with Task-Aware Neural Language Model

Linguistic sequence labeling is a general modeling approach that encompa...

0 Liyuan Liu, et al. ∙

∙ 07/01/2017

Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach

Relation extraction is a fundamental task in information extraction. Mos...

0 Liyuan Liu, et al. ∙

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