Search Results for author: Min Liu

Found 42 papers, 2 papers with code

Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting

no code implementations17 Feb 2023 Qingxiang Liu, Sheng Sun, Min Liu, Yuwei Wang, Bo Gao

In this paper, we perform the first study of forecasting traffic flow adopting Online Learning (OL) manner in FL framework and then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC), aiming to guarantee performance gains regardless of traffic fluctuation.

Federated Learning Graph Attention

Survey of Knowledge Distillation in Federated Edge Learning

no code implementations14 Jan 2023 Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Xuefeng Jiang, Runhan Li, Bo Gao

The increasing demand for intelligent services and privacy protection of mobile and Internet of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL), in which devices collaboratively train on-device Machine Learning (ML) models without sharing their private data.

Knowledge Distillation

A Personalized Utterance Style (PUS) based Dialogue Strategy for Efficient Service Requirement Elicitation

no code implementations7 Jan 2023 Demin Yu, Min Liu, Zhongjie Wang

Considering that traditional dialogue system with static slots cannot be directly applied to the SRE task, it is a challenge to design an efficient dialogue strategy to guide users to express their complete and accurate requirements in such a huge potential requirement space.

FedICT: Federated Multi-task Distillation for Multi-access Edge Computing

no code implementations1 Jan 2023 Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Xuefeng Jiang, Bo Gao

Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC.

Edge-computing Federated Learning +2

THUEE system description for NIST 2020 SRE CTS challenge

no code implementations12 Oct 2022 Yu Zheng, Jinghan Peng, Miao Zhao, Yufeng Ma, Min Liu, Xinyue Ma, Tianyu Liang, Tianlong Kong, Liang He, Minqiang Xu

This paper presents the system description of the THUEE team for the NIST 2020 Speaker Recognition Evaluation (SRE) conversational telephone speech (CTS) challenge.

Speaker Recognition

The SpeakIn System Description for CNSRC2022

no code implementations22 Sep 2022 Yu Zheng, Yihao Chen, Jinghan Peng, Yajun Zhang, Min Liu, Minqiang Xu

In the SV task fixed track, our system was a fusion of five models, and two models were fused in the SV task open track.

Retrieval Speaker Recognition +1

Imbalanced Node Processing Method in Graph Neural Network Classification Task

no code implementations18 Sep 2022 Min Liu, Siwen Jin, Luo Jin, Shuohan Wang, Yu Fang, Yuliang Shi

Therefore, we start with the loss function and try to find a loss function that can effectively solve the imbalance of graph nodes to participate in the node classification task.

Classification Node Classification

Towards Federated Learning against Noisy Labels via Local Self-Regularization

1 code implementation25 Aug 2022 Xuefeng Jiang, Sheng Sun, Yuwei Wang, Min Liu

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner.

Federated Learning Privacy Preserving

FastATDC: Fast Anomalous Trajectory Detection and Classification

no code implementations23 Jul 2022 Tianle Ni, Jingwei Wang, Yunlong Ma, Shuang Wang, Min Liu, Weiming Shen

Here, we present a careful theoretical and empirical analysis of the ATDC algorithm, showing that the calculation of anomaly scores in both stages can be simplified, and that the second stage of the algorithm is much more important than the first stage.


Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation

no code implementations14 Apr 2022 Zhiyuan Wu, Sheng Sun, Min Liu, Junbo Zhang, Yuwei Wang, Qingxiang Liu

Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server periodically aggregates local model parameters from clients without assembling their private data.

Federated Learning Privacy Preserving

Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction

no code implementations3 Apr 2022 Qijin She, Ruizhen Hu, Juzhan Xu, Min Liu, Kai Xu, Hui Huang

To resolve the sample efficiency issue in learning the high-dimensional and complex control of dexterous grasping, we propose an effective representation of grasping state characterizing the spatial interaction between the gripper and the target object.

Bidding Agent Design in the LinkedIn Ad Marketplace

no code implementations25 Feb 2022 Yuan Gao, Kaiyu Yang, Yuanlong Chen, Min Liu, Noureddine El Karoui

We establish a general optimization framework for the design of automated bidding agent in dynamic online marketplaces.

The MSXF TTS System for ICASSP 2022 ADD Challenge

no code implementations27 Jan 2022 Chunyong Yang, PengFei Liu, Yanli Chen, Hongbin Wang, Min Liu

The end to end TTS system is VITS, and the pre-training self-supervised model is wav2vec 2. 0.

Just Least Squares: Binary Compressive Sampling with Low Generative Intrinsic Dimension

no code implementations29 Nov 2021 Yuling Jiao, Dingwei Li, Min Liu, Xiangliang Lu, Yuanyuan Yang

In this paper, we consider recovering $n$ dimensional signals from $m$ binary measurements corrupted by noises and sign flips under the assumption that the target signals have low generative intrinsic dimension, i. e., the target signals can be approximately generated via an $L$-Lipschitz generator $G: \mathbb{R}^k\rightarrow\mathbb{R}^{n}, k\ll n$.

LSNN Method For Scalar Nonlinear HCLs: Discrete Divergence Operator

no code implementations21 Oct 2021 Zhiqiang Cai, Jingshuang Chen, Min Liu

The least-squares neural network (LSNN) method was introduced for solving scalar linear and nonlinear hyperbolic conservation laws in [6, 5].

Numerical Integration

Poformer: A simple pooling transformer for speaker verification

no code implementations10 Oct 2021 Yufeng Ma, Yiwei Ding, Miao Zhao, Yu Zheng, Min Liu, Minqiang Xu

Most recent speaker verification systems are based on extracting speaker embeddings using a deep neural network.

Speaker Verification

DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Blind Detection

no code implementations8 Oct 2021 Xin Zhong, Chen Chen, Shu Fu, Zhihong Zeng, Min Liu

Generalized optical multiple-input multiple-output (GOMIMO) techniques have been recently shown to be promising for high-speed optical wireless communication (OWC) systems.

RitzNet: A Deep Neural Network Method for Linear Stress Problems

no code implementations29 Sep 2021 Min Liu, Zhiqiang Cai, Karthik Ramani

This paper presents RitzNet, an unsupervised learning method which takes any point in the computation domain as input, and learns a neural network model to output its corresponding function value satisfying the underlying governing PDEs.

Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly

no code implementations18 Sep 2021 Yuling Jiao, Dingwei Li, Min Liu, Xiliang Lu

Recovering sparse signals from observed data is an important topic in signal/imaging processing, statistics and machine learning.

Self-adaptive deep neural network: Numerical approximation to functions and PDEs

no code implementations7 Sep 2021 Zhiqiang Cai, Jingshuang Chen, Min Liu

Designing an optimal deep neural network for a given task is important and challenging in many machine learning applications.

Deep Learning-Aided OFDM-Based Generalized Optical Quadrature Spatial Modulation

no code implementations24 Jun 2021 Chen Chen, Lin Zeng, Xin Zhong, Shu Fu, Min Liu, Pengfei Du

In this paper, we propose an orthogonal frequency division multiplexing (OFDM)-based generalized optical quadrature spatial modulation (GOQSM) technique for multiple-input multiple-output optical wireless communication (MIMO-OWC) systems.

Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment

no code implementations9 Jun 2021 Baoyun Peng, Min Liu, Heng Yang, Zhaoning Zhang, Dongsheng Li

Based on the proposed quality measurement, we propose a deep Tiny Face Quality network (tinyFQnet) to learn a quality prediction function from data.

Face Image Quality Face Image Quality Assessment +1

Least-Squares ReLU Neural Network (LSNN) Method For Scalar Nonlinear Hyperbolic Conservation Law

no code implementations25 May 2021 Zhiqiang Cai, Jingshuang Chen, Min Liu

We introduced the least-squares ReLU neural network (LSNN) method for solving the linear advection-reaction problem with discontinuous solution and showed that the method outperforms mesh-based numerical methods in terms of the number of degrees of freedom.

Numerical Integration

Least-Squares ReLU Neural Network (LSNN) Method For Linear Advection-Reaction Equation

no code implementations25 May 2021 Zhiqiang Cai, Jingshuang Chen, Min Liu

This paper studies least-squares ReLU neural network method for solving the linear advection-reaction problem with discontinuous solution.

User Intention Recognition and Requirement Elicitation Method for Conversational AI Services

no code implementations3 Sep 2020 Junrui Tian, Zhiying Tu, Zhongjie Wang, Xiaofei Xu, Min Liu

In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services.

Intent Detection

NOMA for Energy-Efficient LiFi-Enabled Bidirectional IoT Communication

no code implementations20 May 2020 Chen Chen, Shu Fu, Xin Jian, Min Liu, Xiong Deng, Zhiguo Ding

In order to improve the energy efficiency (EE) of the bidirectional LiFi-IoT system, non-orthogonal multiple access (NOMA) with a quality-of-service (QoS)-guaranteed optimal power allocation (OPA) strategy is applied to maximize the EE of the system.

Deep Differentiable Grasp Planner for High-DOF Grippers

no code implementations4 Feb 2020 Min Liu, Zherong Pan, Kai Xu, Kanishka Ganguly, Dinesh Manocha

We present an end-to-end algorithm for training deep neural networks to grasp novel objects.


Deep least-squares methods: an unsupervised learning-based numerical method for solving elliptic PDEs

1 code implementation5 Nov 2019 Zhiqiang Cai, Jingshuang Chen, Min Liu, Xinyu Liu

This paper studies an unsupervised deep learning-based numerical approach for solving partial differential equations (PDEs).

Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-identification

no code implementations27 Aug 2019 Xueping Wang, Rameswar Panda, Min Liu, Yaonan Wang, Amit K. Roy-Chowdhury

Additionally, a cross-view matching strategy followed by global camera network constraints is proposed to explore the matching relationships across the entire camera network.

Graph Matching Metric Learning +2

DAR-Net: Dynamic Aggregation Network for Semantic Scene Segmentation

no code implementations28 Jul 2019 Zongyue Zhao, Min Liu, Karthik Ramani

Traditional grid/neighbor-based static pooling has become a constraint for point cloud geometry analysis.

Scene Segmentation

Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution

no code implementations ICLR 2019 Min Liu, Fupin Yao, Chiho Choi, Sinha Ayan, Karthik Ramani

The ground-breaking performance obtained by deep convolutional neural networks (CNNs) for image processing tasks is inspiring research efforts attempting to extend it for 3D geometric tasks.


Generating Grasp Poses for a High-DOF Gripper Using Neural Networks

no code implementations1 Mar 2019 Min Liu, Zherong Pan, Kai Xu, Kanishka Ganguly, Dinesh Manocha

The quality of the grasp poses is on par with the groundtruth poses in the dataset.


End-to-end Speech Recognition with Adaptive Computation Steps

no code implementations30 Aug 2018 Mohan Li, Min Liu, Masanori Hattori

In this paper, we present Adaptive Computation Steps (ACS) algo-rithm, which enables end-to-end speech recognition models to dy-namically decide how many frames should be processed to predict a linguistic output.

speech-recognition Speech Recognition

ABMOF: A Novel Optical Flow Algorithm for Dynamic Vision Sensors

no code implementations10 May 2018 Min Liu, Tobi Delbruck

The precise event timing, sparse output, and wide dynamic range of the events are well suited for optical flow, but conventional optical flow (OF) algorithms are not well matched to the event stream data.

Optical Flow Estimation

Recurrent 3D Attentional Networks for End-to-End Active Object Recognition

no code implementations14 Oct 2016 Min Liu, Yifei Shi, Lintao Zheng, Kai Xu, Hui Huang, Dinesh Manocha

Active vision is inherently attention-driven: The agent actively selects views to attend in order to fast achieve the vision task while improving its internal representation of the scene being observed.

Object Recognition

Cannot find the paper you are looking for? You can Submit a new open access paper.