Search Results for author:
Found 24 papers, 2 papers with code
Structural-Entropy-Based Sample Selection for Efficient and Effective Learning
Based on the decomposition, we present $\textbf{S}$tructural-$\textbf{E}$ntropy-based sample $\textbf{S}$election ($\textbf{SES}$), a method that integrates both global and local information to select informative and representative samples.
RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers
To ensure the inclusion of anomalous rules, we develop an anomaly-biased model reduction method to prioritize these rules at each hierarchical level.
Foundation Models Meet Visualizations: Challenges and Opportunities
Recent studies have indicated that foundation models, such as BERT and GPT, excel in adapting to a variety of downstream tasks.
A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision
Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection.
TimePool: Visually Answer "Which and When" Questions On Univariate Time Series
When exploring time series datasets, analysts often pose "which and when" questions.
Visual Analytics For Machine Learning: A Data Perspective Survey
The past decade has witnessed a plethora of works that leverage the power of visualization (VIS) to interpret machine learning (ML) models.
Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles
To ease this process, we develop ArchExplorer, a visual analysis method for understanding a neural architecture space and summarizing design principles.
A Unified Understanding of Deep NLP Models for Text Classification
The rapid development of deep natural language processing (NLP) models for text classification has led to an urgent need for a unified understanding of these models proposed individually.
Diagnosing Ensemble Few-Shot Classifiers
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance.
Real-Time Visual Analysis of High-Volume Social Media Posts
In contrast to previous work, our system also works with non-geolocated posts and avoids extensive preprocessing such as detecting events.
Interactive Steering of Hierarchical Clustering
The novelty of our approach includes 1) automatically constructing constraints for hierarchical clustering using knowledge (knowledge-driven) and intrinsic data distribution (data-driven), and 2) enabling the interactive steering of clustering through a visual interface (user-driven).
Visual Analysis of Discrimination in Machine Learning
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.
Diagnosing Concept Drift with Visual Analytics
Concept drift is a phenomenon in which the distribution of a data stream changes over time in unforeseen ways, causing prediction models built on historical data to become inaccurate.
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples
To better analyze and understand the OoD samples in context, we have developed a novel kNN-based grid layout algorithm motivated by Hall's theorem.
Analyzing the Noise Robustness of Deep Neural Networks
The key is to compare and analyze the datapaths of both the adversarial and normal examples.
Recent Research Advances on Interactive Machine Learning
Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems.
Analyzing the Noise Robustness of Deep Neural Networks
Deep neural networks (DNNs) are vulnerable to maliciously generated adversarial examples.
Visual Analytics for Explainable Deep Learning
Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever.
Scalable Inference for Nested Chinese Restaurant Process Topic Models
Finally, we propose an efficient distributed implementation of PCGS through vectorization, pre-processing, and a careful design of the concurrent data structures and communication strategy.
Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective
Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems.
Towards Better Analysis of Deep Convolutional Neural Networks
Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification.
Scaling up Dynamic Topic Models
Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data.
Online Visual Analytics of Text Streams
To this end, we learn a set of streaming tree cuts from topic trees based on user-selected focus nodes.
Tracking Idea Flows between Social Groups
To facilitate users in analyzing the flow, we present a method to model the flow behaviors that aims at identifying the lead-lag relationships between word clusters of different user groups.
