Search Results for author: Pu Wang

Found 18 papers, 6 papers with code

Quantum Feature Extraction for THz Multi-Layer Imaging

no code implementations18 Jul 2022 Toshiaki Koike-Akino, Pu Wang, Genki Yamashita, Wataru Tsujita, Makoto Nakajima

A learning-based THz multi-layer imaging has been recently used for contactless three-dimensional (3D) positioning and encoding.

Machine Learning

Bottleneck Low-rank Transformers for Low-resource Spoken Language Understanding

no code implementations28 Jun 2022 Pu Wang, Hugo Van hamme

End-to-end spoken language understanding (SLU) systems benefit from pretraining on large corpora, followed by fine-tuning on application-specific data.

Spoken Language Understanding

Deep learning on rail profiles matching

1 code implementation18 May 2022 Kunqi Wang, Daolin Si, Pu Wang, Jing Ge, Peiyuan Ni, Shuguo Wang

Matching the rail cross-section profiles measured on site with the designed profile is a must to evaluate the wear of the rail, which is very important for track maintenance and rail safety.

Quantum Transfer Learning for Wi-Fi Sensing

no code implementations17 May 2022 Toshiaki Koike-Akino, Pu Wang, Ye Wang

Beyond data communications, commercial-off-the-shelf Wi-Fi devices can be used to monitor human activities, track device locomotion, and sense the ambient environment.

Transfer Learning

AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications

no code implementations17 May 2022 Toshiaki Koike-Akino, Pu Wang, Ye Wang

Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment.

Machine Learning

Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning

1 code implementation CVPR 2022 Matias Mendieta, Taojiannan Yang, Pu Wang, Minwoo Lee, Zhengming Ding, Chen Chen

To alleviate this issue, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by introducing a variety of proximal terms, some incurring considerable compute and/or memory overheads, to restrain local updates with respect to the global model.

Federated Learning Privacy Preserving

A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose

1 code implementation24 Nov 2021 Ce Zheng, Matias Mendieta, Pu Wang, Aidong Lu, Chen Chen

We propose a pose analysis module that uses graph transformers to exploit structured and implicit joint correlations, and a mesh regression module that combines the extracted pose feature with the mesh template to reconstruct the final human mesh.

3D Human Pose Estimation 3D Human Shape Estimation +2

Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing

no code implementations18 Oct 2021 Pinyarash Pinyoanuntapong, Tagore Pothuneedi, Ravikumar Balakrishnan, Minwoo Lee, Chen Chen, Pu Wang

Federated Learning (FL) over wireless multi-hop edge computing networks, i. e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm.

Edge-computing Federated Learning +2

EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge

no code implementations14 Oct 2021 Pinyarash Pinyoanuntapong, Prabhu Janakaraj, Ravikumar Balakrishnan, Minwoo Lee, Chen Chen, Pu Wang

To solve such MDP, multi-agent reinforcement learning (MA-RL) algorithms along with domain-specific action space refining schemes are developed, which online learn the delay-minimum forwarding paths to minimize the model exchange latency between the edge devices (i. e., workers) and the remote server.

Edge-computing Federated Learning +1

A Study into Pre-training Strategies for Spoken Language Understanding on Dysarthric Speech

no code implementations15 Jun 2021 Pu Wang, Bagher BabaAli, Hugo Van hamme

The acoustic model is pre-trained in two stages: initialization with a corpus of normal speech and finetuning on a mixture of dysarthric and normal speech.

Automatic Speech Recognition speech-recognition +1

MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations

1 code implementation14 May 2021 Taojiannan Yang, Sijie Zhu, Matias Mendieta, Pu Wang, Ravikumar Balakrishnan, Minwoo Lee, Tao Han, Mubarak Shah, Chen Chen

MutualNet is a general training methodology that can be applied to various network structures (e. g., 2D networks: MobileNets, ResNet, 3D networks: SlowFast, X3D) and various tasks (e. g., image classification, object detection, segmentation, and action recognition), and is demonstrated to achieve consistent improvements on a variety of datasets.

Action Recognition Image Classification +2

Pre-training for low resource speech-to-intent applications

no code implementations30 Mar 2021 Pu Wang, Hugo Van hamme

In this paper we combine the encoder of an end-to-end ASR system with the prior NMF/capsule network-based user-taught decoder, and investigate whether pre-training methodology can reduce training data requirements for the NMF and capsule network.

KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

1 code implementation26 Nov 2020 Jiawei Zhu, Xin Han, Hanhan Deng, Chao Tao, Ling Zhao, Pu Wang, Lin Tao, Haifeng Li

On this background, this study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks.

Knowledge Graphs Representation Learning

Deep CSI Learning for Gait Biometric Sensing and Recognition

no code implementations6 Feb 2019 Kalvik Jakkala, Arupjyoti Bhuya, Zhi Sun, Pu Wang, Zhuo Cheng

Gait is a person's natural walking style and a complex biological process that is unique to each person.

Denoising Gait Identification

T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

8 code implementations12 Nov 2018 Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, Haifeng Li

However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence.

Management Traffic Prediction

Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals

no code implementations9 Nov 2013 Jun Fang, Yanning Shen, Hongbin Li, Pu Wang

In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns.

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