1 code implementation • 28 Jan 2024 • Liguo Zhou, Yinglei Song, Yichao Gao, Zhou Yu, Michael Sodamin, Hongshen Liu, Liang Ma, Lian Liu, Hao liu, Yang Liu, Haichuan Li, Guang Chen, Alois Knoll
However, the availability of free and open-source simulators is limited, and the installation and configuration process can be daunting for beginners and interdisciplinary researchers.
no code implementations • 17 Nov 2023 • Xiaoyang Chen, Hao Zheng, Yuemeng Li, Yuncong Ma, Liang Ma, Hongming Li, Yong Fan
A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance.
no code implementations • 17 Jun 2023 • Xiwen Liang, Liang Ma, Shanshan Guo, Jianhua Han, Hang Xu, Shikui Ma, Xiaodan Liang
Understanding and following natural language instructions while navigating through complex, real-world environments poses a significant challenge for general-purpose robots.
no code implementations • 22 May 2023 • Zixiang Han, Lincong Han, Xiaozhou Zhang, Yajuan Wang, Liang Ma, Mengting Lou, Jing Jin, Guangyi Liu
A novel multistatic multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system in cellular networks is proposed.
1 code implementation • 20 Dec 2022 • Liang Ma, Shuyang Cao, Robert L. Logan IV, Di Lu, Shihao Ran, Ke Zhang, Joel Tetreault, Alejandro Jaimes
The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them.
no code implementations • 21 Nov 2022 • Zhiqing Wei, YuAn Wang, Liang Ma, Shaoshi Yang, Zhiyong Feng, Chengkang Pan, Qixun Zhang, Yajuan Wang, Huici Wu, Ping Zhang
In this paper, we investigate how to apply the positioning reference signal (PRS) of the 5th generation (5G) mobile communications in radar sensing.
no code implementations • 13 Jun 2022 • Yilu Guo, Shicai Yang, WeiJie Chen, Liang Ma, Di Xie, ShiLiang Pu
Therefore, it is crucial to study how to learn more discriminative representations while avoiding over-fitting.
no code implementations • 23 May 2022 • Fanfan Ye, Liang Ma, Qiaoyong Zhong, Di Xie, ShiLiang Pu
The knowledge extracted by the delegator is then utilized to maintain the performance of the model on old tasks in incremental learning.
1 code implementation • 31 Mar 2022 • Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, ShiLiang Pu, De-Chuan Zhan
In this work, we propose a new paradigm for FSCIL based on meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT), which synthesizes fake FSCIL tasks from the base dataset.
Ranked #5 on Few-Shot Class-Incremental Learning on CIFAR-100
1 code implementation • CVPR 2022 • Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, ShiLiang Pu, De-Chuan Zhan
Forward compatibility requires future new classes to be easily incorporated into the current model based on the current stage data, and we seek to realize it by reserving embedding space for future new classes.
Ranked #4 on Few-Shot Class-Incremental Learning on CIFAR-100
no code implementations • 14 Mar 2022 • Yan Yan, Tianzheng Liao, Jinjin Zhao, Jiahong Wang, Liang Ma, Wei Lv, Jing Xiong, Lei Wang
Given this observation, we devised a graph-inspired deep learning approach toward the sensor-based HAR tasks, which was further used to build a deep transfer learning model toward giving a tentative solution for these two challenging problems.
no code implementations • NAACL (TextGraphs) 2021 • Sanghamitra Dutta, Liang Ma, Tanay Kumar Saha, Di Lu, Joel Tetreault, Alejandro Jaimes
Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection.
no code implementations • 18 Dec 2020 • Liang Ma
Motivated by such social effect, the concept of influence maximization is coined, where the goal is to select a bounded number of the most influential nodes (seed nodes) from a social network so that they can jointly trigger the maximal influence diffusion.
no code implementations • 18 Dec 2020 • Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, Don Towsley
This is a technical report, containing all the theorem proofs in the following two papers: (1) Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, "Identifiability of Link Metrics Based on End-to-end Path Measurements," in ACM IMC, 2013.
Networking and Internet Architecture
no code implementations • 18 Dec 2020 • Liang Ma
Expert networks are formed by a group of expert-professionals with different specialties to collaboratively resolve specific queries posted to the network.
no code implementations • 17 Dec 2020 • Liang Ma, Ting He, Ananthram Swami, Don Towsley, Kin K. Leung
This is a technical report, containing all the theorem proofs in paper "On Optimal Monitor Placement for Localizing Node Failures via Network Tomography" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IFIP WG 7. 3 Performance, 2015.
Networking and Internet Architecture
no code implementations • 9 Oct 2020 • Paul J. Pritz, Liang Ma, Kin K. Leung
While reinforcement learning has achieved considerable successes in recent years, state-of-the-art models are often still limited by the size of state and action spaces.
Model-based Reinforcement Learning Recommendation Systems +2
no code implementations • 28 Sep 2020 • Paul Julian Pritz, Liang Ma, Kin Leung
Model-free reinforcement learning approaches use some form of state representations and the latest work has explored embedding techniques for actions, both with the aim of achieving better generalization and applicability.
Model-based Reinforcement Learning Recommendation Systems +2
no code implementations • 5 Jun 2020 • Ziyao Zhang, Liang Ma, Kin K. Leung, Konstantinos Poularakis, Mudhakar Srivatsa
We observe that although actions directly define the agents' behaviors, for many problems the next state after a state transition matters more than the action taken, in determining the return of such a state transition.
no code implementations • 26 Apr 2020 • Qi She, Fan Feng, Qi Liu, Rosa H. M. Chan, Xinyue Hao, Chuanlin Lan, Qihan Yang, Vincenzo Lomonaco, German I. Parisi, Heechul Bae, Eoin Brophy, Baoquan Chen, Gabriele Graffieti, Vidit Goel, Hyonyoung Han, Sathursan Kanagarajah, Somesh Kumar, Siew-Kei Lam, Tin Lun Lam, Liang Ma, Davide Maltoni, Lorenzo Pellegrini, Duvindu Piyasena, ShiLiang Pu, Debdoot Sheet, Soonyong Song, Youngsung Son, Zhengwei Wang, Tomas E. Ward, Jianwen Wu, Meiqing Wu, Di Xie, Yangsheng Xu, Lin Yang, Qiaoyong Zhong, Liguang Zhou
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams).
no code implementations • 9 Jan 2020 • Liang Ma, Ziyao Zhang, Mudhakar Srivatsa
Network tomography, a classic research problem in the realm of network monitoring, refers to the methodology of inferring unmeasured network attributes using selected end-to-end path measurements.
no code implementations • 25 Nov 2019 • Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu Aggarwal
In this paper, we present a new class of global string kernels that aims to (i) discover global properties hidden in the strings through global alignments, (ii) maintain positive-definiteness of the kernel, without introducing a diagonal dominant kernel matrix, and (iii) have a training cost linear with respect to not only the length of the string but also the number of training string samples.
no code implementations • ICLR 2019 • Swati Rallapalli, Liang Ma, Mudhakar Srivatsa, Ananthram Swami, Heesung Kwon, Graham Bent, Christopher Simpkin
Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications.
no code implementations • 19 Sep 2019 • Ziyao Zhang, Liang Ma, Konstantinos Poularakis, Kin K. Leung, Jeremy Tucker, Ananthram Swami
In distributed software-defined networks (SDN), multiple physical SDN controllers, each managing a network domain, are implemented to balance centralised control, scalability, and reliability requirements.
no code implementations • 17 Dec 2018 • Yingying Zhang, Qiaoyong Zhong, Liang Ma, Di Xie, ShiLiang Pu
In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively.
1 code implementation • 21 Nov 2018 • Yifan Yang, Qijing Huang, Bichen Wu, Tianjun Zhang, Liang Ma, Giulio Gambardella, Michaela Blott, Luciano Lavagno, Kees Vissers, John Wawrzynek, Kurt Keutzer
DiracDeltaNet achieves competitive accuracy on ImageNet (88. 7\% top-5), but with 42$\times$ fewer parameters and 48$\times$ fewer OPs than VGG16.