Search Results for author: Liang Wu

Found 11 papers, 2 papers with code

Fast and Arbitrary Beam Pattern Design for RIS-Assisted Terahertz Wireless Communication

no code implementations6 May 2022 Jian Dang, Zaichen Zhang, Yewei Li, Liang Wu, Bingcheng Zhu, Lei Wang

Reconfigurable intelligent surface (RIS) can assist terahertz wireless communication to restore the fragile line-of-sight links and facilitate beam steering.

A Novel Two-stage Design Scheme of Equalizers for Uplink FBMC/OQAM-based Massive MIMO Systems

no code implementations4 Dec 2021 Yuhao Qi, Jian Dang, Zaichen Zhang, Liang Wu, Yongpeng Wu

However, existing works show that there remains residual interference after single-tap equalization even with infinite number of BS antennas, leading to a limitation of achievable signal-to-interference-plus-noise ratio (SINR) performance.

Stability and Generalization for Randomized Coordinate Descent

no code implementations17 Aug 2021 Puyu Wang, Liang Wu, Yunwen Lei

Randomized coordinate descent (RCD) is a popular optimization algorithm with wide applications in solving various machine learning problems, which motivates a lot of theoretical analysis on its convergence behavior.

Generalization Bounds

Iterative Distillation for Better Uncertainty Estimates in Multitask Emotion Recognition

no code implementations21 Jul 2021 Didan Deng, Liang Wu, Bertram E. Shi

Iterative distillation over multiple generations significantly improves performance in both emotion recognition and uncertainty estimation.

Emotion Recognition

Fine-grained Generalization Analysis of Vector-valued Learning

no code implementations29 Apr 2021 Liang Wu, Antoine Ledent, Yunwen Lei, Marius Kloft

In this paper, we initiate the generalization analysis of regularized vector-valued learning algorithms by presenting bounds with a mild dependency on the output dimension and a fast rate on the sample size.

Extreme Multi-Label Classification General Classification +3

Editing Text in the Wild

2 code implementations8 Aug 2019 Liang Wu, Chengquan Zhang, Jiaming Liu, Junyu Han, Jingtuo Liu, Errui Ding, Xiang Bai

Specifically, we propose an end-to-end trainable style retention network (SRNet) that consists of three modules: text conversion module, background inpainting module and fusion module.

Image Inpainting Image-to-Image Translation +1

Capturing Evolution Genes for Time Series Data

no code implementations10 May 2019 Wenjie Hu, Yang Yang, Liang Wu, Zongtao Liu, Zhanlin Sun, Bingshen Yao

The modeling of time series is becoming increasingly critical in a wide variety of applications.

Time Series

Data-Rate Driven Transmission Strategy for Deep Learning Based Communication Systems

no code implementations20 Dec 2018 Xiao Chen, Julian Cheng, Zaichen Zhang, Liang Wu, Jian Dang

The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance.

Information Theory Information Theory

Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation

2 code implementations ASONAM 2019 2019 Jundong Li, Liang Wu, Huan Liu

As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the process of feature learning on graphs.

Ensemble Learning Feature Engineering +1

SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification

no code implementations17 Aug 2016 Liang Wu, Fred Morstatter, Huan Liu

To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content.

General Classification Sentiment Analysis

Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval

no code implementations25 Sep 2014 Liang Wu, Hui Xiong, Liang Du, Bo Liu, Guandong Xu, Yong Ge, Yanjie Fu, Yuanchun Zhou, Jianhui Li

Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.

Information Retrieval Metric Learning

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