Search Results for author: Bin He

Found 23 papers, 7 papers with code

FHDe²Net: Full High Definition Demoireing Network

no code implementations ECCV 2020 Bin He, Ce Wang, Boxin Shi, Ling-Yu Duan

Frequency aliasing in the digital capture of display screens leads to the moir´e pattern, appearing as stripe-shaped distortions in images.

Knowledge-enriched Attention Network with Group-wise Semantic for Visual Storytelling

no code implementations10 Mar 2022 Tengpeng Li, Hanli Wang, Bin He, Chang Wen Chen

Third, a unified one-stage story generation model with encoder-decoder structure is proposed to simultaneously train and infer the knowledge-enriched attention network, group-wise semantic module and multi-modal story generation decoder in an end-to-end fashion.

Story Generation Visual Storytelling

Weakly Supervised Disentangled Representation for Goal-conditioned Reinforcement Learning

no code implementations28 Feb 2022 Zhifeng Qian, Mingyu You, Hongjun Zhou, Bin He

In the paper, we propose a skill learning framework DR-GRL that aims to improve the sample efficiency and policy generalization by combining the Disentangled Representation learning and Goal-conditioned visual Reinforcement Learning.

reinforcement-learning Representation Learning

Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects

no code implementations NAACL 2021 Zhiyuan Zhang, Xuancheng Ren, Qi Su, Xu sun, Bin He

Motivated by neuroscientific evidence and theoretical results, we demonstrate that side effects can be controlled by the number of changed parameters and thus, we propose to conduct \textit{neural network surgery} by only modifying a limited number of parameters.

A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models

1 code implementation NAACL 2021 Kaiyuan Liao, Yi Zhang, Xuancheng Ren, Qi Su, Xu sun, Bin He

We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions.

Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models

1 code implementation NAACL 2021 Wenkai Yang, Lei LI, Zhiyuan Zhang, Xuancheng Ren, Xu sun, Bin He

However, in this paper, we find that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples.

Backdoor Attack Data Poisoning +2

DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge

1 code implementation1 Jan 2021 Tianqing Fang, Hongming Zhang, Weiqi Wang, Yangqiu Song, Bin He

On the other hand, generation models have the potential to automatically generate more knowledge.

Text Generation

High-Likelihood Area Matters --- Rewarding Near-Correct Predictions Under Imbalanced Distributions

no code implementations1 Jan 2021 Guangxiang Zhao, Lei LI, Xuancheng Ren, Xu sun, Bin He

We find in practice that the high-likelihood area contains correct predictions for tail classes and it plays a vital role in learning imbalanced class distributions.

KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning

no code implementations7 Dec 2020 Bin He, Xin Jiang, Jinghui Xiao, Qun Liu

Recent studies on pre-trained language models have demonstrated their ability to capture factual knowledge and applications in knowledge-aware downstream tasks.

Language Modelling Machine Reading Comprehension +1

PPKE: Knowledge Representation Learning by Path-based Pre-training

no code implementations7 Dec 2020 Bin He, Di Zhou, Jing Xie, Jinghui Xiao, Xin Jiang, Qun Liu

Entities may have complex interactions in a knowledge graph (KG), such as multi-step relationships, which can be viewed as graph contextual information of the entities.

Link Prediction Representation Learning

Integrating Graph Contextualized Knowledge into Pre-trained Language Models

no code implementations30 Nov 2019 Bin He, Di Zhou, Jinghui Xiao, Xin Jiang, Qun Liu, Nicholas Jing Yuan, Tong Xu

Complex node interactions are common in knowledge graphs, and these interactions also contain rich knowledge information.

Knowledge Graphs Representation Learning

Mop Moire Patterns Using MopNet

1 code implementation ICCV 2019 Bin He, Ce Wang, Boxin Shi, Ling-Yu Duan

The complex frequency distribution, imbalanced magnitude in colour channels, and diverse appearance attributes of moire pattern make its removal a challenging problem.

Image Enhancement

Convolutional Gated Recurrent Units for Medical Relation Classification

no code implementations29 Jul 2018 Bin He, Yi Guan, Rui Dai

Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for relation classification.

Classification General Classification +1

Classifying medical relations in clinical text via convolutional neural networks

no code implementations17 May 2018 Bin He, Yi Guan, Rui Dai

Deep learning research on relation classification has achieved solid performance in the general domain.

Classification General Classification +1

De-identification of medical records using conditional random fields and long short-term memory networks

no code implementations20 Sep 2017 Zhipeng Jiang, Chao Zhao, Bin He, Yi Guan, Jingchi Jiang

The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing focuses on the de-identification of psychiatric evaluation records.


Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records

no code implementations28 Nov 2016 Jia Su, Bin He, Yi Guan, Jingchi Jiang, Jinfeng Yang

To the best of our knowledge, this is the first annotated corpus concerning CVD risk factors in CEMRs and the guidelines for capturing CVD risk factor annotations from CEMRs were proposed.

Building a comprehensive syntactic and semantic corpus of Chinese clinical texts

1 code implementation7 Nov 2016 Bin He, Bin Dong, Yi Guan, Jinfeng Yang, Zhipeng Jiang, Qiubin Yu, Jianyi Cheng, Chunyan Qu

Objective: To build a comprehensive corpus covering syntactic and semantic annotations of Chinese clinical texts with corresponding annotation guidelines and methods as well as to develop tools trained on the annotated corpus, which supplies baselines for research on Chinese texts in the clinical domain.

Active Learning POS

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