Search Results for author: Huamin Qu

Found 22 papers, 5 papers with code

NumGPT: Improving Numeracy Ability of Generative Pre-trained Models

no code implementations7 Sep 2021 Zhihua Jin, Xin Jiang, Xingbo Wang, Qun Liu, Yong Wang, Xiaozhe Ren, Huamin Qu

However, those models do not consider the numerical properties of numbers and cannot perform robustly on numerical reasoning tasks (e. g., math word problems and measurement estimation).

VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models

no code implementations4 Aug 2021 Furui Cheng, Dongyu Liu, Fan Du, Yanna Lin, Alexandra Zytek, Haomin Li, Huamin Qu, Kalyan Veeramachaneni

Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks.

Decision Making

DeHumor: Visual Analytics for Decomposing Humor

no code implementations18 Jul 2021 Xingbo Wang, Yao Ming, Tongshuang Wu, Haipeng Zeng, Yong Wang, Huamin Qu

Despite being a critical communication skill, grasping humor is challenging -- a successful use of humor requires a mixture of both engaging content build-up and an appropriate vocal delivery (e. g., pause).

M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis

no code implementations17 Jul 2021 Xingbo Wang, Jianben He, Zhihua Jin, Muqiao Yang, Yong Wang, Huamin Qu

Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels.

Multimodal Sentiment Analysis

GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks

no code implementations22 Nov 2020 Zhihua Jin, Yong Wang, Qianwen Wang, Yao Ming, Tengfei Ma, Huamin Qu

Two case studies and interviews with domain experts demonstrate the effectiveness of GNNLens in facilitating the understanding of GNN models and their errors.

Node Classification

DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models

no code implementations19 Aug 2020 Furui Cheng, Yao Ming, Huamin Qu

With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable.

Counterfactual Explanation Decision Making

Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network

no code implementations4 Aug 2020 Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin Qu

Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network and further present a new GNN model, called R^2GCN, which intrinsically works for the heterogeneous networks, to achieve generalizable student performance prediction in interactive online question pools.

Visual Analysis of Discrimination in Machine Learning

no code implementations30 Jul 2020 Qianwen Wang, Zhenhua Xu, Zhutian Chen, Yong Wang, Shixia Liu, Huamin Qu

The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning.

Crime Prediction Decision Making +1

HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models

no code implementations12 Feb 2020 Qianwen Wang, William Alexander, Jack Pegg, Huamin Qu, Min Chen

In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models.

Two-sample testing

VoiceCoach: Interactive Evidence-based Training for Voice Modulation Skills in Public Speaking

1 code implementation22 Jan 2020 Xingbo Wang, Haipeng Zeng, Yong Wang, Aoyu Wu, Zhida Sun, Xiaojuan Ma, Huamin Qu

The modulation of voice properties, such as pitch, volume, and speed, is crucial for delivering a successful public speech.

SirenLess: reveal the intention behind news

no code implementations8 Jan 2020 Xumeng Chen, Leo Yu-Ho Lo, Huamin Qu

In this paper, we present SirenLess, a visual analytical system for misleading news detection by linguistic features.

Decision Making

Visual Analytics of Student Learning Behaviors on K-12 Mathematics E-learning Platforms

no code implementations7 Sep 2019 Meng Xia, Huan Wei, Min Xu, Leo Yu Ho Lo, Yong Wang, Rong Zhang, Huamin Qu

With increasing popularity in online learning, a surge of E-learning platforms have emerged to facilitate education opportunities for k-12 (from kindergarten to 12th grade) students and with this, a wealth of information on their learning logs are getting recorded.

LassoNet: Deep Lasso-Selection of 3D Point Clouds

no code implementations31 Jul 2019 Zhutian Chen, Wei Zeng, Zhiguang Yang, Lingyun Yu, Chi-Wing Fu, Huamin Qu

A hierarchical network is trained using a dataset with over 30K lasso-selection records on two different point cloud data.

Human-Computer Interaction Graphics

EmoCo: Visual Analysis of Emotion Coherence in Presentation Videos

no code implementations29 Jul 2019 Haipeng Zeng, Xingbo Wang, Aoyu Wu, Yong Wang, Quan Li, Alex Endert, Huamin Qu

Our visualization system features a channel coherence view and a sentence clustering view that together enable users to obtain a quick overview of emotion coherence and its temporal evolution.

Interpretable and Steerable Sequence Learning via Prototypes

1 code implementation23 Jul 2019 Yao Ming, Panpan Xu, Huamin Qu, Liu Ren

The prediction is obtained by comparing the inputs to a few prototypes, which are exemplar cases in the problem domain.

Sentiment Analysis

DeepDrawing: A Deep Learning Approach to Graph Drawing

no code implementations17 Jul 2019 Yong Wang, Zhihua Jin, Qianwen Wang, Weiwei Cui, Tengfei Ma, Huamin Qu

Node-link diagrams are widely used to facilitate network explorations.

ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

1 code implementation13 Feb 2019 Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni, Huamin Qu

To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts.

AutoML

DeepTracker: Visualizing the Training Process of Convolutional Neural Networks

no code implementations26 Aug 2018 Dongyu Liu, Weiwei Cui, Kai Jin, Yuxiao Guo, Huamin Qu

To bridge this gap and help domain experts with their training tasks in a practical environment, we propose a visual analytics system, DeepTracker, to facilitate the exploration of the rich dynamics of CNN training processes and to identify the unusual patterns that are hidden behind the huge amount of training log.

Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach

no code implementations2 Aug 2018 Hammad Haleem, Yong Wang, Abishek Puri, Sahil Wadhwa, Huamin Qu

In this paper, we present a novel deep learning-based approach to evaluate the readability of graph layouts by directly using graph images.

RuleMatrix: Visualizing and Understanding Classifiers with Rules

1 code implementation17 Jul 2018 Yao Ming, Huamin Qu, Enrico Bertini

With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable.

Understanding Hidden Memories of Recurrent Neural Networks

1 code implementation30 Oct 2017 Yao Ming, Shaozu Cao, Ruixiang Zhang, Zhen Li, Yuanzhe Chen, Yangqiu Song, Huamin Qu

We propose a technique to explain the function of individual hidden state units based on their expected response to input texts.

CNNComparator: Comparative Analytics of Convolutional Neural Networks

no code implementations15 Oct 2017 Haipeng Zeng, Hammad Haleem, Xavier Plantaz, Nan Cao, Huamin Qu

Often, it is difficult to explore the relationships between the learned parameters and the model performance due to a large number of parameters and different random initializations.

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