Search Results for author: Yao Chen

Found 27 papers, 4 papers with code

Towards Automatic Scoring of Spinal X-ray for Ankylosing Spondylitis

no code implementations8 Aug 2023 Yuanhan Mo, Yao Chen, Aimee Readie, Gregory Ligozio, Thibaud Coroller, Bartłomiej W. Papież

In this study, we address this challenge by prototyping a 2-step auto-grading pipeline, called VertXGradeNet, to automatically predict mSASSS scores for the cervical and lumbar vertebral units (VUs) in X-ray spinal imaging.

Federated Learning for Metaverse: A Survey

no code implementations23 Mar 2023 Yao Chen, Shan Huang, Wensheng Gan, Gengsen Huang, Yongdong Wu

In this paper, we review some of the early advances of FL4M, which will be a research direction with unlimited development potential.

Edge-computing Federated Learning +1

VertXNet: An Ensemble Method for Vertebrae Segmentation and Identification of Spinal X-Ray

no code implementations7 Feb 2023 Yao Chen, Yuanhan Mo, Aimee Readie, Gregory Ligozio, Indrajeet Mandal, Faiz Jabbar, Thibaud Coroller, Bartlomiej W. Papiez

Our experimental results have shown that the proposed pipeline outperformed two SOTA segmentation models on our test dataset (MEASURE 1) with a mean Dice of 0. 90, vs. a mean Dice of 0. 73 for Mask R-CNN and 0. 72 for U-Net.

Federated Learning Attacks and Defenses: A Survey

no code implementations27 Nov 2022 Yao Chen, Yijie Gui, Hong Lin, Wensheng Gan, Yongdong Wu

For the purpose of advancing the research in this field, building a robust FL system, and realizing the wide application of FL, this paper sorts out the possible attacks and corresponding defenses of the current FL system systematically.

Federated Learning

Contrast Pattern Mining: A Survey

no code implementations27 Sep 2022 Yao Chen, Wensheng Gan, Yongdong Wu, Philip S. Yu

Contrast pattern mining (CPM) is an important and popular subfield of data mining.

HiKonv: Maximizing the Throughput of Quantized Convolution With Novel Bit-wise Management and Computation

no code implementations22 Jul 2022 Yao Chen, Junhao Pan, Xinheng Liu, JinJun Xiong, Deming Chen

In this study, we propose HiKonv, a unified solution that maximizes the throughput of convolution on a given underlying processing unit with low-bitwidth quantized data inputs through novel bit-wise management and parallel computation.

Management Quantization

VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images

no code implementations12 Jul 2022 Yao Chen, Yuanhan Mo, Aimee Readie, Gregory Ligozio, Thibaud Coroller, Bartlomiej W. Papiez

Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations.

Compilation and Optimizations for Efficient Machine Learning on Embedded Systems

no code implementations6 Jun 2022 Xiaofan Zhang, Yao Chen, Cong Hao, Sitao Huang, Yuhong Li, Deming Chen

Deep Neural Networks (DNNs) have achieved great success in a variety of machine learning (ML) applications, delivering high-quality inferencing solutions in computer vision, natural language processing, and virtual reality, etc.

BIG-bench Machine Learning

HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation

no code implementations28 Dec 2021 Xinheng Liu, Yao Chen, Prakhar Ganesh, Junhao Pan, JinJun Xiong, Deming Chen

Quantization for Convolutional Neural Network (CNN) has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data inputs.

Management Quantization

YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs

1 code implementation26 Oct 2021 Prakhar Ganesh, Yao Chen, Yin Yang, Deming Chen, Marianne Winslett

Performance of object detection models has been growing rapidly on two major fronts, model accuracy and efficiency.

object-detection Real-Time Object Detection +1

Inferential Wasserstein Generative Adversarial Networks

no code implementations13 Sep 2021 Yao Chen, Qingyi Gao, Xiao Wang

The Wasserstein GAN (WGAN) leverages the Wasserstein distance to avoid the caveats in the minmax two-player training of GANs but has other defects such as mode collapse and lack of metric to detect the convergence.

Free Lunch for Co-Saliency Detection: Context Adjustment

no code implementations4 Aug 2021 Lingdong Kong, Prakhar Ganesh, Tan Wang, Junhao Liu, Le Zhang, Yao Chen

We hope that the scale, diversity, and quality of our dataset can benefit researchers in this area and beyond.

Saliency Detection Semantic Segmentation

WinoCNN: Kernel Sharing Winograd Systolic Array for Efficient Convolutional Neural Network Acceleration on FPGAs

1 code implementation9 Jul 2021 Xinheng Liu, Yao Chen, Cong Hao, Ashutosh Dhar, Deming Chen

We implement our proposed accelerator on multiple FPGAs, which outperforms the state-of-the-art designs in terms of both throughput and DSP efficiency.

3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration

no code implementations11 May 2021 Yao Chen, Cole Hawkins, Kaiqi Zhang, Zheng Zhang, Cong Hao

This paper emphasizes the importance and efficacy of training, quantization and accelerator design, and calls for more research breakthroughs in the area for AI on the edge.

Model Compression Quantization

MöbiusE: Knowledge Graph Embedding on Möbius Ring

no code implementations7 Jan 2021 Yao Chen, Jiangang Liu, Zhe Zhang, Shiping Wen, Wenjun Xiong

In this work, we propose a novel Knowledge Graph Embedding (KGE) strategy, called M\"{o}biusE, in which the entities and relations are embedded to the surface of a M\"{o}bius ring.

Knowledge Graph Embedding

Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices

no code implementations14 Oct 2020 Cong Hao, Yao Chen, Xiaofan Zhang, Yuhong Li, JinJun Xiong, Wen-mei Hwu, Deming Chen

High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators.

VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization

1 code implementation18 May 2020 Cheng Gong, Yao Chen, Ye Lu, Tao Li, Cong Hao, Deming Chen

Quantization has been proven to be an effective method for reducing the computing and/or storage cost of DNNs.

Model Compression object-detection +2

EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions

no code implementations6 May 2020 Yuhong Li, Cong Hao, Xiaofan Zhang, Xinheng Liu, Yao Chen, JinJun Xiong, Wen-mei Hwu, Deming Chen

We formulate the co-search problem by fusing DNN search variables and hardware implementation variables into one solution space, and maximize both algorithm accuracy and hardware implementation quality.

Neural Architecture Search

TAG : Type Auxiliary Guiding for Code Comment Generation

no code implementations ACL 2020 Ruichu Cai, Zhihao Liang, Boyan Xu, Zijian Li, Yuexing Hao, Yao Chen

Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e. g., operator, string, etc.

Code Comment Generation Comment Generation +3

Compressing Large-Scale Transformer-Based Models: A Case Study on BERT

no code implementations27 Feb 2020 Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, Marianne Winslett

Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks.

Model Compression

NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving

no code implementations18 Nov 2019 Cong Hao, Yao Chen, Xinheng Liu, Atif Sarwari, Daryl Sew, Ashutosh Dhar, Bryan Wu, Dongdong Fu, JinJun Xiong, Wen-mei Hwu, Junli Gu, Deming Chen

The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-efficiency implementations.

Autonomous Driving

iWGAN: an Autoencoder WGAN for Inference

no code implementations25 Sep 2019 Yao Chen, Qingyi Gao, Xiao Wang

We further provide a rigorous probabilistic interpretation of our model under the framework of maximum likelihood estimation.

A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices

2 code implementations20 May 2019 Xiaofan Zhang, Cong Hao, Yuhong Li, Yao Chen, JinJun Xiong, Wen-mei Hwu, Deming Chen

Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as DNN model inference accuracy, and quality of service (QoS), such as inference latency, throughput, and power consumption.

object-detection Object Detection

Data Poisoning Attack against Unsupervised Node Embedding Methods

no code implementations30 Oct 2018 Mingjie Sun, Jian Tang, Huichen Li, Bo Li, Chaowei Xiao, Yao Chen, Dawn Song

In this paper, we take the task of link prediction as an example, which is one of the most fundamental problems for graph analysis, and introduce a data positioning attack to node embedding methods.

Data Poisoning Link Prediction

Local Region Sparse Learning for Image-on-Scalar Regression

no code implementations27 May 2016 Yao Chen, Xiao Wang, Linglong Kong, Hongtu Zhu

Identification of regions of interest (ROI) associated with certain disease has a great impact on public health.

regression Sparse Learning

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