40 citations · 57 across the 11 of their papers we have counts for
13 papers
ShaRP: Shape-Regularized Multidimensional Projections
Alister Machado, Alexandru Telea, Michael Behrisch
Projections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, each one of them having…
Identifying Cluttering Edges in Near-Planar Graphs
Simon van Wageningen, Tamara Mchedlidze, Alexandru Telea
Planar drawings of graphs tend to be favored over non-planar drawings. Testing planarity and creating a planar layout of a planar graph can be done in linear time. However, creatin…
DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps
Angelos Chatzimparmpas, Rafael M. Martins, Alexandru C. Telea +1
As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and tru…
Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning
Bárbara Caroline Benato, Alexandre Xavier Falcão, Alexandru-Cristian Telea
Lacking supervised data is an issue while training deep neural networks (DNNs), mainly when considering medical and biological data where supervision is expensive. Recently, Embedd…
UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data
Mateus Espadoto, Gabriel Appleby, Ashley Suh +6
Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Altho…
Iterative Pseudo-Labeling with Deep Feature Annotation and Confidence-Based Sampling
Barbara C Benato, Alexandru C Telea, Alexandre X Falcão
Training deep neural networks is challenging when large and annotated datasets are unavailable. Extensive manual annotation of data samples is time-consuming, expensive, and error-…
HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters
Gabriel Appleby, Mateus Espadoto, Rui Chen +4
Projection algorithms such as t-SNE or UMAP are useful for the visualization of high dimensional data, but depend on hyperparameters which must be tuned carefully. Unfortunately, i…
Turbulent Details Simulation for SPH Fluids via Vorticity Refinement
Sinuo Liu, Xiaokun Wang, Xiaojuan Ban +4
A major issue in Smoothed Particle Hydrodynamics (SPH) approaches is the numerical dissipation during the projection process, especially under coarse discretizations. High-frequenc…
Semi-supervised deep learning based on label propagation in a 2D embedded space
Barbara Caroline Benato, Jancarlo Ferreira Gomes, Alexandru Cristian Telea +1
While convolutional neural networks need large labeled sets for training images, expert human supervision of such datasets can be very laborious. Proposed solutions propagate label…
Semi-Automatic Data Annotation guided by Feature Space Projection
Barbara Caroline Benato, Jancarlo Ferreira Gomes, Alexandru Cristian Telea +1
Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few…
Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling
Lucas Pagliosa, Alexandru Telea, Rodrigo Mello
The reconstruction of phase spaces is an essential step to analyze time series according to Dynamical System concepts. A regression performed on such spaces unveils the relationshi…
Quantitative Comparison of Time-Dependent Treemaps
Eduardo Vernier, Max Sondag, Joao Comba +3
Rectangular treemaps are often the method of choice to visualize large hierarchical datasets. Nowadays such datasets are available over time, hence there is a need for (a) treemaps…
Deep Learning Multidimensional Projections
Mateus Espadoto, Nina S. T. Hirata, Alexandru C. Telea
Dimensionality reduction methods, also known as projections, are frequently used for exploring multidimensional data in machine learning, data science, and information visualizatio…