190 citations · 654 across the 20 of their papers we have counts for
20 papers
MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
Zeyang Huang, Takanori Fujiwara, Angelos Chatzimparmpas +2
We present a new nonlinear dimensionality reduction method, MAPLE, that enhances UMAP by improving manifold modeling. MAPLE employs a self-supervised learning approach to more effi…
Adversarial Attacks on Machine Learning-Aided Visualizations
Takanori Fujiwara, Kostiantyn Kucher, Junpeng Wang +3
Research in ML4VIS investigates how to use machine learning (ML) techniques to generate visualizations, and the field is rapidly growing with high societal impact. However, as with…
Simbanex: Similarity-based Exploration of IEEE VIS Publications
Daniel Witschard, Ilir Jusufi, Andreas Kerren
Embeddings are powerful tools for transforming complex and unstructured data into numeric formats suitable for computational analysis tasks. In this work, we use multiple embedding…
Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023
Angelos Chatzimparmpas, Kostiantyn Kucher, Andreas Kerren
Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics…
2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualisations in Virtual Reality
Stefan P. Feyer, Bruno Pinaud, Stephen G. Kobourov +6
Relational information between different types of entities is often modelled by a multilayer network (MLN) -- a network with subnetworks represented by layers. The layers of an MLN…
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…
User Preferences of Spatio-Temporal Referencing Approaches For Immersive 3D Radar Charts
Nico Reski, Aris Alissandrakis, Andreas Kerren
The use of head-mounted display technologies for virtual reality experiences is inherently single-user-centred, allowing for the visual immersion of its user in the computer-genera…
Designing a 3D Gestural Interface to Support User Interaction with Time-Oriented Data as Immersive 3D Radar Chart
Nico Reski, Aris Alissandrakis, Andreas Kerren
The design of intuitive three-dimensional user interfaces is vital for interaction in virtual reality, allowing to effectively close the loop between a human user and the virtual e…
Designing Feature Vector Representations: A case study from Chemistry
Signe Sidwall Thygesen, Daniel Witschard, Andreas Kerren +2
We present a case study investigating feature descriptors in the context of the analysis of chemical multivariate ensemble data. The data of each ensemble member consists of three…
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
A. Chatzimparmpas, R. Martins, I. Jusufi +3
Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, howe…
MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels
Ilya Ploshchik, Angelos Chatzimparmpas, Andreas Kerren
Stacking (or stacked generalization) is an ensemble learning method with one main distinctiveness from the rest: even though several base models are trained on the original data se…
HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques
Angelos Chatzimparmpas, Fernando V. Paulovich, Andreas Kerren
Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques…
VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees
Angelos Chatzimparmpas, Rafael M. Martins, Andreas Kerren
Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these met…
FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher +1
The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data, including complex feature engineering processes…
VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher +1
During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process is computationally intensive and requires an e…
StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher +1
In machine learning (ML), ensemble methods such as bagging, boosting, and stacking are widely-established approaches that regularly achieve top-notch predictive performance. Stacki…
Xtreaming: an incremental multidimensional projection technique and its application to streaming data
Tácito T. A. T. Neves, Rafael M. Martins, Danilo B. Coimbra +3
Streaming data applications are becoming more common due to the ability of different information sources to continuously capture or produce data, such as sensors and social media.…
t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
Angelos Chatzimparmpas, Rafael M. Martins, Andreas Kerren
t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range…
A Study of Mental Maps in Immersive Network Visualization
Joseph Kotlarek, Oh-Hyun Kwon, Kwan-Liu Ma +4
The visualization of a network influences the quality of the mental map that the viewer develops to understand the network. In this study, we investigate the effects of a 3D immers…
Proceedings of the 26th International Symposium on Graph Drawing and Network Visualization (GD 2018)
Therese Biedl, Andreas Kerren
These are the revised accepted papers from the 26th International Symposium on Graph Drawing and Network Visualization (GD 2018), Barcelona, Spain, September 26 - September 28, 201…