Search Results for author: Yifei Shen

Found 18 papers, 8 papers with code

Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks

no code implementations10 May 2022 Wentao Yu, Yifei Shen, Hengtao He, Xianghao Yu, Jun Zhang, Khaled B. Letaief

We draw inspirations from fixed point theory to develop an efficient deep learning based channel estimator with adaptive complexity and linear convergence guarantee.

Graph Neural Networks for Wireless Communications: From Theory to Practice

1 code implementation21 Mar 2022 Yifei Shen, Jun Zhang, S. H. Song, Khaled B. Letaief

For design guidelines, we propose a unified framework that is applicable to general design problems in wireless networks, which includes graph modeling, neural architecture design, and theory-guided performance enhancement.

Computer Vision

Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets

1 code implementation9 Mar 2022 Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu

This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.

An Empirical Study of Graphormer on Large-Scale Molecular Modeling Datasets

no code implementations28 Feb 2022 Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu

This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.

Neural Piecewise-Constant Delay Differential Equations

no code implementations4 Jan 2022 Qunxi Zhu, Yifei Shen, Dongsheng Li, Wei Lin

Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neural networks and dynamical systems.

Reinforcement Learning Enhanced Explainer for Graph Neural Networks

no code implementations NeurIPS 2021 Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, Dongsheng Li

To address these issues, we propose a RL-enhanced GNN explainer, RG-Explainer, which consists of three main components: starting point selection, iterative graph generation and stopping criteria learning.

Combinatorial Optimization Graph Generation +1

Learn to Communicate with Neural Calibration: Scalability and Generalization

no code implementations1 Oct 2021 Yifan Ma, Yifei Shen, Xianghao Yu, Jun Zhang, S. H. Song, Khaled B. Letaief

Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models.

How Powerful is Graph Convolution for Recommendation?

1 code implementation17 Aug 2021 Yifei Shen, Yongji Wu, Yao Zhang, Caihua Shan, Jun Zhang, Khaled B. Letaief, Dongsheng Li

In this paper, we endeavor to obtain a better understanding of GCN-based CF methods via the lens of graph signal processing.

Collaborative Filtering

Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation

no code implementations3 Aug 2021 Yifan Ma, Yifei Shen, Xianghao Yu, Jun Zhang, S. H. Song, Khaled B. Letaief

Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems.

Invariant Information Bottleneck for Domain Generalization

no code implementations11 Jun 2021 Bo Li, Yifei Shen, Yezhen Wang, Wenzhen Zhu, Colorado J. Reed, Jun Zhang, Dongsheng Li, Kurt Keutzer, Han Zhao

IIB significantly outperforms IRM on synthetic datasets, where the pseudo-invariant features and geometric skews occur, showing the effectiveness of proposed formulation in overcoming failure modes of IRM.

Domain Generalization

Decentralized Statistical Inference with Unrolled Graph Neural Networks

1 code implementation4 Apr 2021 He Wang, Yifei Shen, Ziyuan Wang, Dongsheng Li, Jun Zhang, Khaled B. Letaief, Jie Lu

In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples without centralized coordination.

Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis

1 code implementation15 Jul 2020 Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief

In this paper, we propose to apply graph neural networks (GNNs) to solve large-scale radio resource management problems, supported by effective neural network architecture design and theoretical analysis.

Distributed Optimization

Blind Data Detection in Massive MIMO via $\ell_3$-norm Maximization over the Stiefel Manifold

no code implementations26 Apr 2020 Ye Xue, Yifei Shen, Vincent Lau, Jun Zhang, Khaled B. Letaief

Specifically, we propose a novel $\ell_3$-norm-based formulation to recover the data without channel estimation.

Complete Dictionary Learning via $\ell_p$-norm Maximization

1 code implementation24 Feb 2020 Yifei Shen, Ye Xue, Jun Zhang, Khaled B. Letaief, Vincent Lau

Dictionary learning is a classic representation learning method that has been widely applied in signal processing and data analytics.

Dictionary Learning Representation Learning

A Graph Neural Network Approach for Scalable Wireless Power Control

2 code implementations19 Jul 2019 Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief

Specifically, a $K$-user interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph.

LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples

no code implementations18 Dec 2018 Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief

To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL, which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples.

Imitation Learning Transfer Learning

Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks

no code implementations17 Nov 2018 Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief

A unique advantage of the proposed method is that it can tackle the task mismatch issue with a few additional unlabeled training samples, which is especially important when transferring to large-size problems.

Transfer Learning

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