no code implementations • 29 May 2024 • Xixi Wu, Yifei Shen, Caihua Shan, Kaitao Song, Siwei Wang, Bohang Zhang, Jiarui Feng, Hong Cheng, Wei Chen, Yun Xiong, Dongsheng Li
Task planning is emerging as an important research topic alongside the development of large language models (LLMs).
no code implementations • 15 May 2024 • Siwei Wang, Yifei Shen, Shi Feng, Haoran Sun, Shang-Hua Teng, Wei Chen
In this paper, we present the findings of our Project ALPINE which stands for ``Autoregressive Learning for Planning In NEtworks."
no code implementations • 10 May 2024 • Hanchi Sun, Xiaohong Liu, Xinyang Jiang, Yifei Shen, Dongsheng Li, Xiongkuo Min, Guangtao Zhai
This paper focuses on the task of quality enhancement for compressed videos.
1 code implementation • 30 Apr 2024 • Weiquan Huang, Yifei Shen, Yifan Yang
State space models and Mamba-based models have been increasingly applied across various domains, achieving state-of-the-art performance.
1 code implementation • 19 Mar 2024 • Yifei Shen, Xinyang Jiang, Yezhen Wang, Yifan Yang, Dongqi Han, Dongsheng Li
Adding additional control to pretrained diffusion models has become an increasingly popular research area, with extensive applications in computer vision, reinforcement learning, and AI for science.
no code implementations • 10 Dec 2023 • William Wei Wang, Dongqi Han, Xufang Luo, Yifei Shen, Charles Ling, Boyu Wang, Dongsheng Li
Empowering embodied agents, such as robots, with Artificial Intelligence (AI) has become increasingly important in recent years.
no code implementations • 25 Oct 2023 • Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, Dongsheng Li
In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes.
no code implementations • 6 Jul 2023 • Yifei Shen, Jiawei Shao, Xinjie Zhang, Zehong Lin, Hao Pan, Dongsheng Li, Jun Zhang, Khaled B. Letaief
The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world.
no code implementations • 1 Jun 2023 • Ruibin Li, Qihua Zhou, Song Guo, Jie Zhang, Jingcai Guo, Xinyang Jiang, Yifei Shen, Zhenhua Han
Diffusion-based Generative Models (DGMs) have achieved unparalleled performance in synthesizing high-quality visual content, opening up the opportunity to improve image super-resolution (SR) tasks.
1 code implementation • International Conference on Learning Representations 2023 • Ziyue Li, Kan Ren, Xinyang Jiang, Yifei Shen, Haipeng Zhang, Dongsheng Li
Moreover, our method is highly efficient and achieves more than 1000 times training speedup compared to the conventional DG methods with fine-tuning a pretrained model.
Ranked #1 on Domain Generalization on PACS
1 code implementation • 8 Dec 2022 • Cairong Zhao, Yubin Wang, Xinyang Jiang, Yifei Shen, Kaitao Song, Dongsheng Li, Duoqian Miao
Prompt learning is one of the most effective and trending ways to adapt powerful vision-language foundation models like CLIP to downstream datasets by tuning learnable prompt vectors with very few samples.
Ranked #4 on Prompt Engineering on Caltech-101
1 code implementation • 29 Nov 2022 • Wentao Yu, Yifei Shen, Hengtao He, Xianghao Yu, Shenghui Song, Jun Zhang, Khaled B. Letaief
For practical usage, the proposed framework is further extended to wideband THz UM-MIMO systems with beam squint effect.
1 code implementation • 8 Jun 2022 • Bo Li, Yifei Shen, Jingkang Yang, Yezhen Wang, Jiawei Ren, Tong Che, Jun Zhang, Ziwei Liu
It is motivated by an empirical finding that transformer-based models trained with empirical risk minimization (ERM) outperform CNN-based models employing state-of-the-art (SOTA) DG algorithms on multiple DG datasets.
Ranked #15 on Domain Generalization on DomainNet (using extra training data)
1 code implementation • 10 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.
1 code implementation • 21 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.
3 code implementations • 9 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.
no code implementations • 28 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.
no code implementations • 4 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.
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.
2 code implementations • NeurIPS 2021 • Xinyang Jiang, Lu Liu, Caihua Shan, Yifei Shen, Xuanyi Dong, Dongsheng Li
In this paper, we consider a different data format for images: vector graphics.
no code implementations • 1 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.
1 code implementation • 17 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.
Ranked #8 on Collaborative Filtering on Gowalla
no code implementations • 3 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.
no code implementations • 11 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.
1 code implementation • 4 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.
1 code implementation • 15 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.
no code implementations • 26 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.
1 code implementation • 24 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.
2 code implementations • 19 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.
no code implementations • 18 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.
no code implementations • 17 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.