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-…