Search Results for author: Liang Ma

Found 21 papers, 3 papers with code

5G PRS-Based Sensing: A Sensing Reference Signal Approach for Joint Sensing and Communication System

no code implementations21 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.

Autonomous Driving

Self-distilled Knowledge Delegator for Exemplar-free Class Incremental Learning

no code implementations23 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.

class-incremental learning Incremental Learning

Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks

1 code implementation31 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.

class-incremental learning Incremental Learning +1

Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition

no code implementations14 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.

Few-Shot Learning Human Activity Recognition +1

Forward Compatible Few-Shot Class-Incremental Learning

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.

class-incremental learning Incremental Learning

GTN-ED: Event Detection Using Graph Transformer Networks

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.

Event Detection

Query Answering via Decentralized Search

no code implementations18 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.

Identification of Additive Link Metrics: Proof of Selected Theorems

no code implementations18 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

Influence Maximization Under Generic Threshold-based Non-submodular Model

no code implementations18 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.

Node Failure Localization: Theorem Proof

no code implementations17 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

Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning

no code implementations9 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 +1

Jointly-Trained State-Action Embedding for Efficient Reinforcement Learning

no code implementations28 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 +1

State Action Separable Reinforcement Learning

no code implementations5 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.

Decision Making reinforcement-learning

Neural Network Tomography

no code implementations9 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.

Data Augmentation

Efficient Global String Kernel with Random Features: Beyond Counting Substructures

no code implementations25 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.

MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design

no code implementations19 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.

reinforcement-learning

Learning Incremental Triplet Margin for Person Re-identification

no code implementations17 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.

Metric Learning Person Re-Identification

Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs

1 code implementation21 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.

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