Search Results for author: Rui Hu

Found 30 papers, 4 papers with code

Echoes: Unsupervised Debiasing via Pseudo-bias Labeling in an Echo Chamber

no code implementations6 May 2023 Rui Hu, Yahan Tu, Jitao Sang

Subsequently, we use the inverse of the sample weights of the biased model as the sample weights for training the target model.

Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service

no code implementations15 Mar 2023 Muhammad Jahanzeb Khan, Rui Hu, Mohammad Sadoghi, Dongfang Zhao

To evaluate this approach, the authors develop a framework called Data-Decoupling Federated Learning (DDFL) and compare it with state-of-the-art FL systems that tightly couple data management and computation.

Federated Learning Management

STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction

no code implementations8 Mar 2023 Rui Hu, Jianan Cui, Chengjin Yu, YunMei Chen, Huafeng Liu

Dynamic positron emission tomography (dPET) image reconstruction is extremely challenging due to the limited counts received in individual frame.

Image Reconstruction

LMPDNet: TOF-PET list-mode image reconstruction using model-based deep learning method

no code implementations21 Feb 2023 Chenxu Li, Rui Hu, Jianan Cui, Huafeng Liu

Additionally, we compare the spatial and temporal consumption of list-mode data and sinogram data in model-based deep learning methods, demonstrating the superiority of list-mode data in model-based TOF-PET reconstruction.

Image Reconstruction

FasterX: Real-Time Object Detection Based on Edge GPUs for UAV Applications

no code implementations7 Sep 2022 Wei Zhou, Xuanlin Min, Rui Hu, Yiwen Long, Huan Luo, JunYi

Real-time object detection on Unmanned Aerial Vehicles (UAVs) is a challenging issue due to the limited computing resources of edge GPU devices as Internet of Things (IoT) nodes.

object-detection Real-Time Object Detection

TransEM:Residual Swin-Transformer based regularized PET image reconstruction

no code implementations9 May 2022 Rui Hu, Huafeng Liu

Positron emission tomography(PET) image reconstruction is an ill-posed inverse problem and suffers from high level of noise due to limited counts received.

Image Reconstruction

Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy

no code implementations15 Feb 2022 Rui Hu, Yanmin Gong, Yuanxiong Guo

Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keeping their training data locally has received great attention recently and can protect privacy in comparison with the traditional centralized learning paradigm.

Federated Learning

When Creators Meet the Metaverse: A Survey on Computational Arts

no code implementations26 Nov 2021 Lik-Hang Lee, Zijun Lin, Rui Hu, Zhengya Gong, Abhishek Kumar, Tangyao Li, Sijia Li, Pan Hui

The metaverse, enormous virtual-physical cyberspace, has brought unprecedented opportunities for artists to blend every corner of our physical surroundings with digital creativity.

Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Data

no code implementations17 Nov 2021 Jitao Sang, Jinqiang Wang, Rui Hu, Chaoquan Jiang

Deep network models perform excellently on In-Distribution (ID) data, but can significantly fail on Out-Of-Distribution (OOD) data.

Hybrid Local SGD for Federated Learning with Heterogeneous Communications

no code implementations ICLR 2022 Yuanxiong Guo, Ying Sun, Rui Hu, Yanmin Gong

Communication is a key bottleneck in federated learning where a large number of edge devices collaboratively learn a model under the orchestration of a central server without sharing their own training data.

Federated Learning

PLUMENet: Efficient 3D Object Detection from Stereo Images

1 code implementation17 Jan 2021 Yan Wang, Bin Yang, Rui Hu, Ming Liang, Raquel Urtasun

In this paper we propose a model that unifies these two tasks and performs them in the same metric space.

3D Object Detection From Stereo Images Depth Estimation +1

StrObe: Streaming Object Detection from LiDAR Packets

no code implementations12 Nov 2020 Davi Frossard, Simon Suo, Sergio Casas, James Tu, Rui Hu, Raquel Urtasun

In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built.

object-detection Object Detection

Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning

no code implementations11 Sep 2020 Rui Hu, Yanmin Gong

Federated Learning rests on the notion of training a global model distributedly on various devices.

Federated Learning

Conditional Entropy Coding for Efficient Video Compression

no code implementations ECCV 2020 Jerry Liu, Shenlong Wang, Wei-Chiu Ma, Meet Shah, Rui Hu, Pranaab Dhawan, Raquel Urtasun

We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames.


Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization

no code implementations1 Aug 2020 Rui Hu, Yanmin Gong, Yuanxiong Guo

Since sparsification would increase the number of communication rounds required to achieve a certain target accuracy, which is unfavorable for DP guarantee, we further introduce acceleration techniques to help reduce the privacy cost.

Federated Learning

Learning Lane Graph Representations for Motion Forecasting

1 code implementation ECCV 2020 Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun

We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions.

Motion Forecasting Trajectory Prediction

MultiXNet: Multiclass Multistage Multimodal Motion Prediction

no code implementations3 Jun 2020 Nemanja Djuric, Henggang Cui, Zhaoen Su, Shangxuan Wu, Huahua Wang, Fang-Chieh Chou, Luisa San Martin, Song Feng, Rui Hu, Yang Xu, Alyssa Dayan, Sidney Zhang, Brian C. Becker, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Carl K. Wellington

One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future.

motion prediction

Concentrated Differentially Private and Utility Preserving Federated Learning

no code implementations30 Mar 2020 Rui Hu, Yuanxiong Guo, Yanmin Gong

Federated learning is a machine learning setting where a set of edge devices collaboratively train a model under the orchestration of a central server without sharing their local data.

Federated Learning Privacy Preserving

Differentially Private Federated Learning for Resource-Constrained Internet of Things

no code implementations28 Mar 2020 Rui Hu, Yuanxiong Guo, E. Paul. Ratazzi, Yanmin Gong

With the proliferation of smart devices having built-in sensors, Internet connectivity, and programmable computation capability in the era of Internet of things (IoT), tremendous data is being generated at the network edge.

Federated Learning

PolyTransform: Deep Polygon Transformer for Instance Segmentation

no code implementations CVPR 2020 Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Yuwen Xiong, Rui Hu, Raquel Urtasun

In this paper, we propose PolyTransform, a novel instance segmentation algorithm that produces precise, geometry-preserving masks by combining the strengths of prevailing segmentation approaches and modern polygon-based methods.

 Ranked #1 on Instance Segmentation on Cityscapes test (using extra training data)

Instance Segmentation Semantic Segmentation

DP-ADMM: ADMM-based Distributed Learning with Differential Privacy

no code implementations30 Aug 2018 Zonghao Huang, Rui Hu, Yuanxiong Guo, Eric Chan-Tin, Yanmin Gong

The goal of this paper is to provide differential privacy for ADMM-based distributed machine learning.

BIG-bench Machine Learning

Determining Points on Handwritten Mathematical Symbols

no code implementations20 Jun 2013 Rui Hu, Stephen M. Watt

In a variety of applications, such as handwritten mathematics and diagram labelling, it is common to have symbols of many different sizes in use and for the writing not to follow simple baselines.

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