no code implementations • 18 Mar 2024 • Fei Ni, Rongpeng Li, Zhifeng Zhao, Honggang Zhang
In particular, different from the conventional CRC, we introduce a topological data analysis (TDA)-based error detection method, which capably digs out the inner topological and geometric information of images, so as to capture semantic information and determine the necessity for re-transmission.
no code implementations • 6 Mar 2024 • Yibin Chen, Yifu Yuan, Zeyu Zhang, Yan Zheng, Jinyi Liu, Fei Ni, Jianye Hao
To bridge the gap with the real-world requirements, we introduce $\textbf{SheetRM}$, a benchmark featuring long-horizon and multi-category tasks with reasoning-dependent manipulation caused by real-life challenges.
no code implementations • 22 Feb 2024 • Jinyi Liu, Yifu Yuan, Jianye Hao, Fei Ni, Lingzhi Fu, Yibin Chen, Yan Zheng
Recently, there has been considerable attention towards leveraging large language models (LLMs) to enhance decision-making processes.
no code implementations • 27 Jan 2024 • Zibin Dong, Jianye Hao, Yifu Yuan, Fei Ni, Yitian Wang, Pengyi Li, Yan Zheng
Diffusion planning has been recognized as an effective decision-making paradigm in various domains.
no code implementations • 18 Jan 2024 • Fei Ni, Bingyan Wang, Rongpeng Li, Zhifeng Zhao, Honggang Zhang
In the swiftly advancing realm of communication technologies, Semantic Communication (SemCom), which emphasizes knowledge understanding and processing, has emerged as a hot topic.
no code implementations • 3 Oct 2023 • Zibin Dong, Yifu Yuan, Jianye Hao, Fei Ni, Yao Mu, Yan Zheng, Yujing Hu, Tangjie Lv, Changjie Fan, Zhipeng Hu
Aligning agent behaviors with diverse human preferences remains a challenging problem in reinforcement learning (RL), owing to the inherent abstractness and mutability of human preferences.
no code implementations • 31 May 2023 • Fei Ni, Jianye Hao, Yao Mu, Yifu Yuan, Yan Zheng, Bin Wang, Zhixuan Liang
Recently, diffusion model shines as a promising backbone for the sequence modeling paradigm in offline reinforcement learning(RL).
1 code implementation • 3 Feb 2023 • Zhixuan Liang, Yao Mu, Mingyu Ding, Fei Ni, Masayoshi Tomizuka, Ping Luo
For example, AdaptDiffuser not only outperforms the previous art Diffuser by 20. 8% on Maze2D and 7. 5% on MuJoCo locomotion, but also adapts better to new tasks, e. g., KUKA pick-and-place, by 27. 9% without requiring additional expert data.
1 code implementation • 9 Oct 2022 • Yao Mu, Yuzheng Zhuang, Fei Ni, Bin Wang, Jianyu Chen, Jianye Hao, Ping Luo
This paper addresses such a challenge by Decomposed Mutual INformation Optimization (DOMINO) for context learning, which explicitly learns a disentangled context to maximize the mutual information between the context and historical trajectories, while minimizing the state transition prediction error.
no code implementations • 2 Oct 2022 • Yifu Yuan, Jianye Hao, Fei Ni, Yao Mu, Yan Zheng, Yujing Hu, Jinyi Liu, Yingfeng Chen, Changjie Fan
Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks.
no code implementations • 25 Feb 2022 • Jiahui Duan, Xialiang Tong, Fei Ni, Zhenan He, Lei Chen, Mingxuan Yuan
The bin packing problem exists widely in real logistic scenarios (e. g., packing pipeline, express delivery), with its goal to improve the packing efficiency and reduce the transportation cost.
no code implementations • 20 Jan 2020 • Kun He, Kevin Tole, Fei Ni, Yong Yuan, Linyun Liao
We address a new variant of packing problem called the circle bin packing problem (CBPP), which is to find a dense packing of circle items to multiple square bins so as to minimize the number of used bins.
no code implementations • 26 Apr 2019 • Rongpeng Li, Zhifeng Zhao, Xing Xu, Fei Ni, Honggang Zhang
Afterwards, we highlight the potential huge impact of CI on both communications and intelligence.