Search Results for author: Yaming Yang

Found 19 papers, 5 papers with code

Privacy-preserving Online AutoML for Domain-Specific Face Detection

no code implementations16 Mar 2022 Chenqian Yan, Yuge Zhang, Quanlu Zhang, Yaming Yang, Xinyang Jiang, Yuqing Yang, Baoyuan Wang

Thanks to HyperFD, each local task (client) is able to effectively leverage the learning "experience" of previous tasks without uploading raw images to the platform; meanwhile, the meta-feature extractor is continuously learned to better trade off the bias and variance.

AutoML Face Detection

Learning Multi-granularity User Intent Unit for Session-based Recommendation

1 code implementation25 Dec 2021 Jiayan Guo, Yaming Yang, Xiangchen Song, Yuan Zhang, Yujing Wang, Jing Bai, Yan Zhang

Specifically, we creatively propose Multi-granularity Intent Heterogeneous Session Graph which captures the interactions between different granularity intent units and relieves the burden of long-dependency.

Session-Based Recommendations

SkipNode: On Alleviating Over-smoothing for Deep Graph Convolutional Networks

no code implementations22 Dec 2021 Weigang Lu, Yibing Zhan, Ziyu Guan, Liu Liu, Baosheng Yu, Wei Zhao, Yaming Yang, DaCheng Tao

Analytically, 1) skipping the convolutional operation prevents the features from losing diversity; and 2) the "skipped" nodes enable gradients to be directly passed back, thus mitigating the gradient vanishing and model weights over-decaying issues.

Link Prediction Node Classification

Multimodal Dialogue Response Generation

no code implementations ACL 2022 Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang

In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model.

Dialogue Generation Response Generation

Graph Pointer Neural Networks

no code implementations3 Oct 2021 Tianmeng Yang, Yujing Wang, Zhihan Yue, Yaming Yang, Yunhai Tong, Jing Bai

On the one hand, multi-hop-based approaches do not explicitly distinguish relevant nodes from a large number of multi-hop neighborhoods, leading to a severe over-smoothing problem.

Node Classification

WRENCH: A Comprehensive Benchmark for Weak Supervision

1 code implementation23 Sep 2021 Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, Alexander Ratner

To address these problems, we introduce a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.

Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation

no code implementations5 Sep 2021 Yankai Chen, Yaming Yang, Yujing Wang, Jing Bai, Xiangchen Song, Irwin King

However, simply integrating KGs in current KG-based RS models is not necessarily a guarantee to improve the recommendation performance, which may even weaken the holistic model capability.

Click-Through Rate Prediction Knowledge-Aware Recommendation +1

Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees

1 code implementation EACL 2021 Jiangang Bai, Yujing Wang, Yiren Chen, Yaming Yang, Jing Bai, Jing Yu, Yunhai Tong

Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information.

Natural Language Understanding

Evolving Attention with Residual Convolutions

no code implementations20 Feb 2021 Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yunhai Tong

In this paper, we propose a novel and generic mechanism based on evolving attention to improve the performance of transformers.

Image Classification Machine Translation +2

Predictive Attention Transformer: Improving Transformer with Attention Map Prediction

no code implementations1 Jan 2021 Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Yunhai Tong

Instead, we model their dependencies via a chain of prediction models that take previous attention maps as input to predict the attention maps of a new layer through convolutional neural networks.

14 Machine Translation

How Does Supernet Help in Neural Architecture Search?

no code implementations16 Oct 2020 Yuge Zhang, Quanlu Zhang, Yaming Yang

Weight sharing, as an approach to speed up architecture performance estimation has received wide attention.

Neural Architecture Search

Interpretable and Efficient Heterogeneous Graph Convolutional Network

1 code implementation27 May 2020 Yaming Yang, Ziyu Guan, Jian-Xin Li, Wei Zhao, Jiangtao Cui, Quan Wang

However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore all possible meta-paths and extract the most useful ones for a target object, which hinders both effectiveness and interpretability; (2) they often need to generate intermediate meta-path based dense graphs, which leads to high computational complexity.

LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression

no code implementations COLING 2020 Yihuan Mao, Yujing Wang, Chufan Wu, Chen Zhang, Yang Wang, Yaming Yang, Quanlu Zhang, Yunhai Tong, Jing Bai

BERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks.

Knowledge Distillation Model Compression +1

Deeper Insights into Weight Sharing in Neural Architecture Search

1 code implementation6 Jan 2020 Yuge Zhang, Zejun Lin, Junyang Jiang, Quanlu Zhang, Yujing Wang, Hui Xue, Chen Zhang, Yaming Yang

With the success of deep neural networks, Neural Architecture Search (NAS) as a way of automatic model design has attracted wide attention.

Neural Architecture Search

DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition

no code implementations10 Oct 2019 Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang, Zhichao Han, Bo Li, Wei Min, Xi Rao, Hansheng Ren, Yinan Shan, Yingxia Shao, Yujie Wang, Fan Wu, Hui Xue, Yaming Yang, Zitao Zhang, Yang Zhao, Shuai Zhang, Yujing Wang, Bin Cui, Ce Zhang

Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.

Cannot find the paper you are looking for? You can Submit a new open access paper.