Search Results for author: Yanjun Ma

Found 21 papers, 17 papers with code

SaGE: 基于句法感知图卷积神经网络和ELECTRA的中文隐喻识别模型(SaGE: Syntax-aware GCN with ELECTRA for Chinese Metaphor Detection)

no code implementations CCL 2021 Shenglong Zhang, Ying Liu, Yanjun Ma

“隐喻是人类语言中经常出现的一种特殊现象, 隐喻识别对于自然语言处理各项任务来说具有十分基础和重要的意义。针对中文领域的隐喻识别任务, 我们提出了一种基于句法感知图卷积神经网络和ELECTRA的隐喻识别模型(Syntax-aware GCN withELECTRA SaGE)。该模型从语言学出发, 使用ELECTRA和Transformer编码器抽取句子的语义特征, 将句子按照依存关系组织成一张图并使用图卷积神经网络抽取其句法特征, 在此基础上对两类特征进行融合以进行隐喻识别。我们的模型在CCL2018中文隐喻识别评测数据集上以85. 22%的宏平均F1分数超越了此前的最佳成绩, 验证了融合语义信息和句法信息对于隐喻识别任务具有重要作用。”

A Gentle Introduction to Deep Nets and Opportunities for the Future

no code implementations ACL 2022 Kenneth Church, Valia Kordoni, Gary Marcus, Ernest Davis, Yanjun Ma, Zeyu Chen

The first half of this tutorial will make deep nets more accessible to a broader audience, following “Deep Nets for Poets” and “A Gentle Introduction to Fine-Tuning.” We will also introduce GFT (general fine tuning), a little language for fine tuning deep nets with short (one line) programs that are as easy to code as regression in statistics packages such as R using glm (general linear models).

SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022

1 code implementation8 Aug 2022 Jingbo Zhou, Xinjiang Lu, Yixiong Xiao, Jiantao Su, Junfu Lyu, Yanjun Ma, Dejing Dou

Thus, Wind Power Forecasting (WPF) has been widely recognized as one of the most critical issues in wind power integration and operation.

HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle

1 code implementation12 Jul 2022 Guoxia Wang, Xiaomin Fang, Zhihua Wu, Yiqun Liu, Yang Xue, Yingfei Xiang, dianhai yu, Fan Wang, Yanjun Ma

Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to implement the training and inference of AlphaFold2 from scratch.

Protein Structure Prediction

PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System

1 code implementation7 Jun 2022 Chenxia Li, Weiwei Liu, Ruoyu Guo, Xiaoting Yin, Kaitao Jiang, Yongkun Du, Yuning Du, Lingfeng Zhu, Baohua Lai, Xiaoguang Hu, dianhai yu, Yanjun Ma

For text recognizer, the base model is replaced from CRNN to SVTR, and we introduce lightweight text recognition network SVTR LCNet, guided training of CTC by attention, data augmentation strategy TextConAug, better pre-trained model by self-supervised TextRotNet, UDML, and UIM to accelerate the model and improve the effect.

Data Augmentation Optical Character Recognition +2

SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System

1 code implementation20 May 2022 Liang Shen, Zhihua Wu, Weibao Gong, Hongxiang Hao, Yangfan Bai, HuaChao Wu, Xinxuan Wu, Jiang Bian, Haoyi Xiong, dianhai yu, Yanjun Ma

With the increasing diversity of ML infrastructures nowadays, distributed training over heterogeneous computing systems is desired to facilitate the production of big models.

Distributed Computing

Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters

1 code implementation19 May 2022 Yang Xiang, Zhihua Wu, Weibao Gong, Siyu Ding, Xianjie Mo, Yuang Liu, Shuohuan Wang, Peng Liu, Yongshuai Hou, Long Li, Bin Wang, Shaohuai Shi, Yaqian Han, Yue Yu, Ge Li, Yu Sun, Yanjun Ma, dianhai yu

We took natural language processing (NLP) as an example to show how Nebula-I works in different training phases that include: a) pre-training a multilingual language model using two remote clusters; and b) fine-tuning a machine translation model using knowledge distilled from pre-trained models, which run through the most popular paradigm of recent deep learning.

Cross-Lingual Natural Language Inference Distributed Computing +2

End-to-end Adaptive Distributed Training on PaddlePaddle

1 code implementation6 Dec 2021 Yulong Ao, Zhihua Wu, dianhai yu, Weibao Gong, Zhiqing Kui, Minxu Zhang, Zilingfeng Ye, Liang Shen, Yanjun Ma, Tian Wu, Haifeng Wang, Wei Zeng, Chao Yang

The experiments demonstrate that our framework can satisfy various requirements from the diversity of applications and the heterogeneity of resources with highly competitive performance.

Language Modelling Recommendation Systems +1

PP-LCNet: A Lightweight CPU Convolutional Neural Network

8 code implementations17 Sep 2021 Cheng Cui, Tingquan Gao, Shengyu Wei, Yuning Du, Ruoyu Guo, Shuilong Dong, Bin Lu, Ying Zhou, Xueying Lv, Qiwen Liu, Xiaoguang Hu, dianhai yu, Yanjun Ma

We propose a lightweight CPU network based on the MKLDNN acceleration strategy, named PP-LCNet, which improves the performance of lightweight models on multiple tasks.

Image Classification object-detection +2

PAFNet: An Efficient Anchor-Free Object Detector Guidance

1 code implementation28 Apr 2021 Ying Xin, Guanzhong Wang, Mingyuan Mao, Yuan Feng, Qingqing Dang, Yanjun Ma, Errui Ding, Shumin Han

Therefore, a trade-off between effectiveness and efficiency is necessary in practical scenarios.

 Ranked #1 on Object Detection on COCO test-dev (Hardware Burden metric)

object-detection Object Detection

PP-YOLOv2: A Practical Object Detector

1 code implementation21 Apr 2021 Xin Huang, Xinxin Wang, Wenyu Lv, Xiaying Bai, Xiang Long, Kaipeng Deng, Qingqing Dang, Shumin Han, Qiwen Liu, Xiaoguang Hu, dianhai yu, Yanjun Ma, Osamu Yoshie

To meet these two concerns, we comprehensively evaluate a collection of existing refinements to improve the performance of PP-YOLO while almost keep the infer time unchanged.

Real-Time Object Detection

Answer-focused and Position-aware Neural Question Generation

no code implementations EMNLP 2018 Xingwu Sun, Jing Liu, Yajuan Lyu, wei he, Yanjun Ma, Shi Wang

(2) The model copies the context words that are far from and irrelevant to the answer, instead of the words that are close and relevant to the answer.

Machine Reading Comprehension Question Answering +2

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