Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Yuxin Ma

Yuxin Ma
Alert button

Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks


Apr 18, 2023
Ping Gong, Yuxin Ma, Cheng Li, Xiaosong Ma, Sam H. Noh

Add code
Alert button

Figure 1 for Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks
Figure 2 for Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks
Figure 3 for Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks
Figure 4 for Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis


Jan 25, 2023
Yingchaojie Feng, Xingbo Wang, Bo Pan, Kam Kwai Wong, Yi Ren, Shi Liu, Zihan Yan, Yuxin Ma, Huamin Qu, Wei Chen

Add code
Alert button

Figure 1 for XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis
Figure 2 for XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis
Figure 3 for XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis
Figure 4 for XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis
* 14 pages, 7 figures. A preprint version of a publication at IEEE Transactions on Visualization and Computer Graphics (TVCG), 2023 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

BiFeat: Supercharge GNN Training via Graph Feature Quantization


Jul 29, 2022
Yuxin Ma, Ping Gong, Jun Yi, Zhewei Yao, Minjie Wang, Cheng Li, Yuxiong He, Feng Yan

Add code
Alert button

Figure 1 for BiFeat: Supercharge GNN Training via Graph Feature Quantization
Figure 2 for BiFeat: Supercharge GNN Training via Graph Feature Quantization
Figure 3 for BiFeat: Supercharge GNN Training via Graph Feature Quantization
Figure 4 for BiFeat: Supercharge GNN Training via Graph Feature Quantization

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

CNN-Augmented Visual-Inertial SLAM with Planar Constraints


May 05, 2022
Pan Ji, Yuan Tian, Qingan Yan, Yuxin Ma, Yi Xu

Add code
Alert button

Figure 1 for CNN-Augmented Visual-Inertial SLAM with Planar Constraints
Figure 2 for CNN-Augmented Visual-Inertial SLAM with Planar Constraints
Figure 3 for CNN-Augmented Visual-Inertial SLAM with Planar Constraints
Figure 4 for CNN-Augmented Visual-Inertial SLAM with Planar Constraints

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras


May 05, 2022
Qingan Yan, Pan Ji, Nitin Bansal, Yuxin Ma, Yuan Tian, Yi Xu

Add code
Alert button

Figure 1 for FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras
Figure 2 for FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras
Figure 3 for FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras
Figure 4 for FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping


May 03, 2022
Pan Ji, Qingan Yan, Yuxin Ma, Yi Xu

Add code
Alert button

Figure 1 for GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping
Figure 2 for GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping
Figure 3 for GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping
Figure 4 for GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes


Sep 15, 2020
Yuxin Ma, Arlen Fan, Jingrui He, Arun Reddy Nelakurthi, Ross Maciejewski

Add code
Alert button

Figure 1 for A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
Figure 2 for A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
Figure 3 for A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
Figure 4 for A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
* Accepted to IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VAST 2020) 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics


Jul 20, 2019
Yuxin Ma, Tiankai Xie, Jundong Li, Ross Maciejewski

Add code
Alert button

Figure 1 for Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Figure 2 for Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Figure 3 for Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Figure 4 for Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
* Under review 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email