Search Results for author: Yuxin Ma

Found 11 papers, 6 papers with code

GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation

1 code implementation25 Aug 2023 Fan Lei, Yuxin Ma, Stewart Fotheringham, Elizabeth Mack, ZiQi Li, Mehak Sachdeva, Sarah Bardin, Ross Maciejewski

As analysts create their spatial models, our framework flags potential issues with model parameter selections, utilizes template-based text generation to summarize model outputs, and links with external knowledge repositories to provide annotations that help to explain the model results.

regression Text Generation

A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization

1 code implementation10 Aug 2023 Yansong Huang, Zherui Zhang, Ao Jiao, Yuxin Ma, Ran Cheng

Evolutionary multi-objective optimization (EMO) algorithms have been demonstrated to be effective in solving multi-criteria decision-making problems.

Benchmarking Decision Making

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

no code implementations18 Apr 2023 Ping Gong, Yuxin Ma, Cheng Li, Xiaosong Ma, Sam H. Noh

In this paper, we primarily focus on understanding the data preprocessing pipeline for DNN Training in the public cloud.

XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis

no code implementations25 Jan 2023 Yingchaojie Feng, Xingbo Wang, Bo Pan, Kam Kwai Wong, Yi Ren, Shi Liu, Zihan Yan, Yuxin Ma, Huamin Qu, Wei Chen

Our research explores how to provide explanations for NLIs to help users locate the problems and further revise the queries.

Data Visualization

BiFeat: Supercharge GNN Training via Graph Feature Quantization

1 code implementation29 Jul 2022 Yuxin Ma, Ping Gong, Jun Yi, Zhewei Yao, Cheng Li, Yuxiong He, Feng Yan

We identify the main accuracy impact factors in graph feature quantization and theoretically prove that BiFeat training converges to a network where the loss is within $\epsilon$ of the optimal loss of uncompressed network.


CNN-Augmented Visual-Inertial SLAM with Planar Constraints

no code implementations5 May 2022 Pan Ji, Yuan Tian, Qingan Yan, Yuxin Ma, Yi Xu

The CNN depth effectively bootstraps the back-end optimization of SLAM and meanwhile the CNN uncertainty adaptively weighs the contribution of each feature point to the back-end optimization.

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

no code implementations5 May 2022 Qingan Yan, Pan Ji, Nitin Bansal, Yuxin Ma, Yuan Tian, Yi Xu

In this paper, we deal with the problem of monocular depth estimation for fisheye cameras in a self-supervised manner.

Monocular Depth Estimation

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

no code implementations3 May 2022 Pan Ji, Qingan Yan, Yuxin Ma, Yi Xu

We present a robust and accurate depth refinement system, named GeoRefine, for geometrically-consistent dense mapping from monocular sequences.

Optical Flow Estimation

Self-Supervised Vessel Segmentation via Adversarial Learning

1 code implementation ICCV 2021 Yuxin Ma, Yang Hua, Hanming Deng, Tao Song, Hao Wang, Zhengui Xue, Heng Cao, Ruhui Ma, Haibing Guan

Vessel segmentation is critically essential for diagnosinga series of diseases, e. g., coronary artery disease and retinal disease.

Domain Adaptation

A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes

1 code implementation15 Sep 2020 Yuxin Ma, Arlen Fan, Jingrui He, Arun Reddy Nelakurthi, Ross Maciejewski

Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time.

Descriptive Image Classification +1

Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

1 code implementation17 Jul 2019 Yuxin Ma, Tiankai Xie, Jundong Li, Ross Maciejewski

Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks.

BIG-bench Machine Learning Data Poisoning

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