1 code implementation • 24 Apr 2024 • Zhixiong Yang, Jingyuan Xia, Shengxi Li, Xinghua Huang, Shuanghui Zhang, Zhen Liu, Yaowen Fu, Yongxiang Liu
This paper proposes an unsupervised kernel estimation model, named dynamic kernel prior (DKP), to realize an unsupervised and pre-training-free learning-based algorithm for solving the BSR problem.
no code implementations • 24 Oct 2022 • Jing-Yuan Xia, Zhixiong Yang, Tong Qiu, Huaizhang Liao, Deniz Gunduz
Multi-user multiple-input multiple-output (MU-MIMO) beamforming design is typically formulated as a non-convex weighted sum rate (WSR) maximization problem that is known to be NP-hard.
1 code implementation • 1 Sep 2022 • Zhixiong Yang, Junwen Pan, Yanzhan Yang, Xiaozhou Shi, Hong-Yu Zhou, Zhicheng Zhang, Cheng Bian
The overall framework, namely as Prototype-aware Contrastive learning (ProCo), is unified as a single-stage pipeline in an end-to-end manner to alleviate the imbalanced problem in medical image classification, which is also a distinct progress than existing works as they follow the traditional two-stage pipeline.
1 code implementation • 21 Jun 2022 • Zhixiong Yang, Jing-Yuan Xia, Junshan Luo, Shuanghui Zhang, Deniz Gündüz
This paper proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming.
no code implementations • 12 Mar 2022 • Jun-Jie Huang, Tianrui Liu, Zhixiong Yang, Shaojing Fu, Wentao Zhao, Pier Luigi Dragotti
With the deep unrolling technique, we build the DURRNet with ProxNets to model natural image priors and ProxInvNets which are constructed with invertible networks to impose the exclusion prior.
2 code implementations • 13 May 2021 • Qinkai Zheng, Houyi Li, Peng Zhang, Zhixiong Yang, Guowei Zhang, Xintan Zeng, Yongchao Liu
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation.
Ranked #1 on Node Property Prediction on ogbn-proteins
no code implementations • 23 Aug 2019 • Zhixiong Yang, Arpita Gang, Waheed U. Bajwa
While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorithmic framework.
2 code implementations • 21 Aug 2019 • Cheng Fang, Zhixiong Yang, Waheed U. Bajwa
The focus of this paper is on robustification of decentralized learning in the presence of nodes that have undergone Byzantine failures.
no code implementations • 28 Aug 2017 • Zhixiong Yang, Waheed U. Bajwa
Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location.