b'Xiaowen Chu'

research

∙ 03/03/2023

FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training

Federated Learning (FL) enables collaborations among clients for train m...

2 Zhenheng Tang, et al. ∙

research

∙ 02/24/2023

Decoupling the All-Reduce Primitive for Accelerating Distributed Deep Learning

Communication scheduling has been shown to be effective in accelerating ...

0 Lin Zhang, et al. ∙

research

∙ 11/30/2022

Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity

Existing learning-based multi-view stereo (MVS) methods rely on the dept...

0 Qingsong Yan, et al. ∙

research

∙ 11/23/2022

NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

One-shot neural architecture search (NAS) substantially improves the sea...

0 Xin He, et al. ∙

research

∙ 08/29/2022

SphereDepth: Panorama Depth Estimation from Spherical Domain

The panorama image can simultaneously demonstrate complete information o...

6 Qingsong Yan, et al. ∙

research

∙ 07/20/2022

EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching

Recent advanced studies have spent considerable human efforts on optimiz...

0 Qiang Wang, et al. ∙

research

∙ 06/06/2022

Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning

In federated learning (FL), model performance typically suffers from cli...

0 Zhenheng Tang, et al. ∙

research

∙ 05/30/2022

Learning Adaptive Propagation for Knowledge Graph Reasoning

Due to the success of Graph Neural Networks (GNNs) in learning from grap...

0 Yongqi Zhang, et al. ∙

research

∙ 11/30/2021

EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs

Generative Adversarial Networks (GANs) have been proven hugely successfu...

0 Guohao Ying, et al. ∙

research

∙ 11/22/2021

FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

Federated Learning (FL) is a distributed learning paradigm that can lear...

2 Chaoyang He, et al. ∙

research

∙ 10/12/2021

Embracing Structure in Data for Billion-Scale Semantic Product Search

We present principled approaches to train and deploy dyadic neural embed...

0 Vihan Lakshman, et al. ∙

research

∙ 10/06/2021

FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

Deep neural networks (DNNs) have achieved great success in the area of c...

9 Qiang Wang, et al. ∙

research

∙ 06/27/2021

A Comprehensive Survey of Incentive Mechanism for Federated Learning

Federated learning utilizes various resources provided by participants t...

0 Rongfei Zeng, et al. ∙

research

∙ 04/01/2021

Energy-aware Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous Clusters

Energy conservation of large data centers for high-performance computing...

0 Xinxin Mei, et al. ∙

research

∙ 01/26/2021

Efficient Multi-objective Evolutionary 3D Neural Architecture Search for COVID-19 Detection with Chest CT Scans

COVID-19 pandemic has spread globally for months. Due to its long incuba...

0 Xin He, et al. ∙

research

∙ 01/24/2021

BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing

The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecede...

0 Zhe Peng, et al. ∙

research

∙ 01/14/2021

Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans

The COVID-19 pandemic has spread globally for several months. Because it...

0 Xin He, et al. ∙

research

∙ 10/20/2020

Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters

Distributed training techniques have been widely deployed in large-scale...

0 Shaohuai Shi, et al. ∙

research

∙ 08/13/2020

Performance Characterization and Bottleneck Analysis of Hyperledger Fabric

Hyperledger Fabric is a popular open-source project for deploying permis...

0 Canhui Wang, et al. ∙

research

∙ 05/29/2020

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Multiplication of a sparse matrix to a dense matrix (SpDM) is widely use...

0 Shaohuai Shi, et al. ∙

research

∙ 05/27/2020

Communication-Efficient Distributed Deep Learning: Survey, Evaluation, and Challenges

In recent years, distributed deep learning techniques are widely deploye...

0 Shaohuai Shi, et al. ∙

research

∙ 03/24/2020

FADNet: A Fast and Accurate Network for Disparity Estimation

Deep neural networks (DNNs) have achieved great success in the area of c...

13 Qiang Wang, et al. ∙

research

∙ 03/10/2020

Communication-Efficient Distributed Deep Learning: A Comprehensive Survey

Distributed deep learning becomes very common to reduce the overall trai...

0 Zhenheng Tang, et al. ∙

research

∙ 02/24/2020

Communication Contention Aware Scheduling of Multiple Deep Learning Training Jobs

Distributed Deep Learning (DDL) has rapidly grown its popularity since i...

0 Qiang Wang, et al. ∙

research

∙ 02/22/2020

FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC

Promising federated learning coupled with Mobile Edge Computing (MEC) is...

0 Rongfei Zeng, et al. ∙

research

∙ 02/22/2020

Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection

Distributed learning techniques such as federated learning have enabled ...

0 Zhenheng Tang, et al. ∙

research

∙ 02/18/2020

A Survey of Deep Learning Techniques for Neural Machine Translation

In recent years, natural language processing (NLP) has got great develop...

0 Shuoheng Yang, et al. ∙

research

∙ 12/20/2019

IRS: A Large Synthetic Indoor Robotics Stereo Dataset for Disparity and Surface Normal Estimation

Indoor robotics localization, navigation and interaction heavily rely on...

2 Qiang Wang, et al. ∙

research

∙ 12/18/2019

MG-WFBP: Merging Gradients Wisely for Efficient Communication in Distributed Deep Learning

Distributed synchronous stochastic gradient descent has been widely used...

0 Shaohuai Shi, et al. ∙

research

∙ 11/20/2019

Understanding Top-k Sparsification in Distributed Deep Learning

Distributed stochastic gradient descent (SGD) algorithms are widely depl...

0 Shaohuai Shi, et al. ∙

research

∙ 11/20/2019

Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees

To reduce the long training time of large deep neural network (DNN) mode...

0 Shaohuai Shi, et al. ∙

research

∙ 11/20/2019

Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

Skin disease is one of the most common types of human diseases, which ma...

18 Xin He, et al. ∙

research

∙ 09/15/2019

Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Deep neural networks (DNNs) have become widely used in many AI applicati...

0 Yuxin Wang, et al. ∙

research

∙ 08/02/2019

AutoML: A Survey of the State-of-the-Art

Deep learning has penetrated all aspects of our lives and brought us gre...

257 Xin He, et al. ∙

research

∙ 05/27/2019

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Over the past years, great progress has been made in improving the compu...

0 Zhenheng Tang, et al. ∙

research

∙ 02/20/2019

Measurement and Analysis of the Bitcoin Networks: A View from Mining Pools

Mining pools, the main components of the Bitcoin network, dominate the c...

0 Canhui Wang, et al. ∙

research

∙ 02/14/2019

GPU Accelerated Keccak (SHA3) Algorithm

Hash functions like SHA-1 or MD5 are one of the most important cryptogra...

0 Canhui Wang, et al. ∙

research

∙ 02/14/2019

GPU Accelerated AES Algorithm

It has been widely accepted that Graphics Processing Units (GPU) is one ...

0 Canhui Wang, et al. ∙

research

∙ 01/14/2019

A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networks

Distributed synchronous stochastic gradient descent (S-SGD) with data pa...

0 Shaohuai Shi, et al. ∙

research

∙ 11/27/2018

MG-WFBP: Efficient Data Communication for Distributed Synchronous SGD Algorithms

Distributed synchronous stochastic gradient descent has been widely used...

0 Shaohuai Shi, et al. ∙

research

∙ 07/30/2018

Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes

Synchronized stochastic gradient descent (SGD) optimizers with data para...

0 Xianyan Jia, et al. ∙

research

∙ 05/10/2018

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

With huge amounts of training data, deep learning has made great breakth...

0 Shaohuai Shi, et al. ∙

research

∙ 11/16/2017

Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs

Deep learning frameworks have been widely deployed on GPU servers for de...

0 Shaohuai Shi, et al. ∙

research

∙ 11/09/2017

Performance Evaluation of Deep Learning Tools in Docker Containers

With the success of deep learning techniques in a broad range of applica...

0 Pengfei Xu, et al. ∙

research

∙ 01/19/2017

GPGPU Performance Estimation with Core and Memory Frequency Scaling

Graphics Processing Units (GPUs) support dynamic voltage and frequency s...

0 Qiang Wang, et al. ∙

Xiaowen Chu

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