Search Results for author: Jing Zhou

Found 31 papers, 9 papers with code

Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective

1 code implementation20 Jun 2024 Yunfei Liu, Jintang Li, Yuehe Chen, Ruofan Wu, Ericbk Wang, Jing Zhou, Sheng Tian, Shuheng Shen, Xing Fu, Changhua Meng, Weiqiang Wang, Liang Chen

Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks.

Clustering Community Detection +3

Target Speaker Extraction by Directly Exploiting Contextual Information in the Time-Frequency Domain

no code implementations27 Feb 2024 Xue Yang, Changchun Bao, Jing Zhou, Xianhong Chen

These weighting matrices reflect the similarity among different frames of the T-F representations and are further employed to obtain the consistent T-F representations of the enrollment.

Target Speaker Extraction

LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning

no code implementations28 Nov 2023 Jintang Li, Jiawang Dan, Ruofan Wu, Jing Zhou, Sheng Tian, Yunfei Liu, Baokun Wang, Changhua Meng, Weiqiang Wang, Yuchang Zhu, Liang Chen, Zibin Zheng

Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data.

Graph Learning

Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs

no code implementations18 Oct 2023 Jintang Li, Zheng Wei, Jiawang Dan, Jing Zhou, Yuchang Zhu, Ruofan Wu, Baokun Wang, Zhang Zhen, Changhua Meng, Hong Jin, Zibin Zheng, Liang Chen

Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily.

Node Classification Representation Learning

Adaptive Fusion of Radiomics and Deep Features for Lung Adenocarcinoma Subtype Recognition

no code implementations27 Aug 2023 Jing Zhou, Xiaotong Fu, Xirong Li, Wei Feng, Zhang Zhang, Ying Ji

The most common type of lung cancer, lung adenocarcinoma (LUAD), has been increasingly detected since the advent of low-dose computed tomography screening technology.

Quantized control of non-Lipschitz nonlinear systems: a novel control framework with prescribed transient performance and lower design complexity

no code implementations28 Nov 2022 Zongcheng Liu, Jiangshuai Huang, Changyun Wen, Jing Zhou, Xiucai Huang

A novel control design framework is proposed for a class of non-Lipschitz nonlinear systems with quantized states, meanwhile prescribed transient performance and lower control design complexity could be guaranteed.

Quantization

Embedding Compression for Text Classification Using Dictionary Screening

no code implementations23 Nov 2022 Jing Zhou, Xinru Jing, Muyu Liu, Hansheng Wang

This leads to a benchmark model, which we then use to obtain the predicted class probabilities for each sample in a dataset.

text-classification Text Classification

A Universal Discriminator for Zero-Shot Generalization

1 code implementation15 Nov 2022 Haike Xu, Zongyu Lin, Jing Zhou, Yanan Zheng, Zhilin Yang

In the finetuning setting, our approach also achieves new state-of-the-art results on a wide range of NLP tasks, with only 1/4 parameters of previous methods.

Zero-shot Generalization

A Universal PINNs Method for Solving Partial Differential Equations with a Point Source

1 code implementation Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence 2022 Xiang Huang, Hongsheng Liu, Beiji Shi, Zidong Wang, Kang Yang, Yang Li, Min Wang, Haotian Chu, Jing Zhou, Fan Yu, Bei Hua, Bin Dong, Lei Chen

In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs)method emerges to be a promising method for solving both forward and inverse PDE problems.

Multi-source wideband doa estimation method by frequency focusing and error weighting

no code implementations28 Mar 2022 Jing Zhou, Changchun Bao

One is that the sub-array decomposition is adopted to improve the accuracy of MSW-DOA estimation by minimizing the weighted error, and the other one is that the frequency focusing procedure is optimized according to the presence probability of sound sources for reducing the influence of the sub-bands with low signal to noise ratio (SNR).

Automatic Product Copywriting for E-Commerce

no code implementations15 Dec 2021 Xueying Zhang, Yanyan Zou, Hainan Zhang, Jing Zhou, Shiliang Diao, Jiajia Chen, Zhuoye Ding, Zhen He, Xueqi He, Yun Xiao, Bo Long, Han Yu, Lingfei Wu

It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening.

Product Recommendation Text Generation

Residual fourier neural operator for thermochemical curing of composites

no code implementations15 Nov 2021 Gengxiang Chen, Yingguang Li, Xu Liu, Qinglu Meng, Jing Zhou, Xiaozhong Hao

During the curing process of composites, the temperature history heavily determines the evolutions of the field of degree of cure as well as the residual stress, which will further influence the mechanical properties of composite, thus it is important to simulate the real temperature history to optimize the curing process of composites.

Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks

no code implementations2 Nov 2021 Xiang Huang, Hongsheng Liu, Beiji Shi, Zidong Wang, Kang Yang, Yang Li, Bingya Weng, Min Wang, Haotian Chu, Jing Zhou, Fan Yu, Bei Hua, Lei Chen, Bin Dong

In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems.

Nonparametric C- and D-vine based quantile regression

no code implementations9 Feb 2021 Marija Tepegjozova, Jing Zhou, Gerda Claeskens, Claudia Czado

Further, we show that the nonparametric conditional quantile estimator is consistent.

Methodology

Holographic Schwinger Effect in Anisotropic Media

no code implementations20 Jan 2021 Jing Zhou, Jialun Ping

This observation implies that the Schwinger effect within an anisotropic background is comparatively weaker when contrasted with its manifestation in an isotropic background. Finally, we also find that the Schwinger effect in the transverse direction is weakened compared to the parallel direction in the anisotropic background, which is consistent with the top-down model.

High Energy Physics - Theory

Factor Normalization for Deep Neural Network Models

1 code implementation1 Jan 2021 Haobo Qi, Jing Zhou, Hansheng Wang

Deep neural network (DNN) models often involve features of ultrahigh dimensions.

Detangling robustness in high dimensions: composite versus model-averaged estimation

no code implementations12 Jun 2020 Jing Zhou, Gerda Claeskens, Jelena Bradic

We find, however, that model-averaged and composite quantile estimators often outperform least-squares methods, even in the case of Gaussian model noise.

Vocal Bursts Intensity Prediction

Benchmark Tests of Convolutional Neural Network and Graph Convolutional Network on HorovodRunner Enabled Spark Clusters

1 code implementation12 May 2020 Jing Pan, Wendao Liu, Jing Zhou

The freedom of fast iterations of distributed deep learning tasks is crucial for smaller companies to gain competitive advantages and market shares from big tech giants.

Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space

no code implementations ICML 2020 Keizo Kato, Jing Zhou, Tomotake Sasaki, Akira Nakagawa

We show our method has the following properties: (i) the Jacobian matrix between the input space and a Euclidean latent space forms a constantlyscaled orthonormal system and enables isometric data embedding; (ii) the relation of PDFs in both spaces can become tractable one such as proportional relation.

Relation Unsupervised Anomaly Detection

RATE-DISTORTION OPTIMIZATION GUIDED AUTOENCODER FOR GENERATIVE APPROACH

no code implementations25 Sep 2019 Keizo Kato, Jing Zhou, Akira Nakagawa

In the generative model approach of machine learning, it is essential to acquire an accurate probabilistic model and compress the dimension of data for easy treatment.

Unsupervised Anomaly Detection

Long-Term Memory Networks for Question Answering

no code implementations6 Jul 2017 Fenglong Ma, Radha Chitta, Saurabh Kataria, Jing Zhou, Palghat Ramesh, Tong Sun, Jing Gao

Question answering is an important and difficult task in the natural language processing domain, because many basic natural language processing tasks can be cast into a question answering task.

Question Answering

Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

no code implementations19 Jun 2017 Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, Jing Gao

Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results.

SYSTRAN's Pure Neural Machine Translation Systems

no code implementations18 Oct 2016 Josep Crego, Jungi Kim, Guillaume Klein, Anabel Rebollo, Kathy Yang, Jean Senellart, Egor Akhanov, Patrice Brunelle, Aurelien Coquard, Yongchao Deng, Satoshi Enoue, Chiyo Geiss, Joshua Johanson, Ardas Khalsa, Raoum Khiari, Byeongil Ko, Catherine Kobus, Jean Lorieux, Leidiana Martins, Dang-Chuan Nguyen, Alexandra Priori, Thomas Riccardi, Natalia Segal, Christophe Servan, Cyril Tiquet, Bo wang, Jin Yang, Dakun Zhang, Jing Zhou, Peter Zoldan

Since the first online demonstration of Neural Machine Translation (NMT) by LISA, NMT development has recently moved from laboratory to production systems as demonstrated by several entities announcing roll-out of NMT engines to replace their existing technologies.

Machine Translation NMT +1

Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video

no code implementations3 May 2016 Jing Zhou, Xiaopeng Hong, Fei Su, Guoying Zhao

To overcome this problem, we propose a real-time regression framework based on the recurrent convolutional neural network for automatic frame-level pain intensity estimation.

Pain Intensity Regression regression

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