David Auber
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View article: Visualizing, Exploring and Analyzing Big Data: A 6-Year Story
Visualizing, Exploring and Analyzing Big Data: A 6-Year Story Open
Information Visualization has been one of the cornerstones of Data Science, turning the abundance of Big Data being produced through modern systems into actionable knowledge. Indeed, the Big Data era has realized the availability of volumi…
View article: Towards a partial order graph for interactive pharmacophore exploration: extraction of pharmacophores activity delta
Towards a partial order graph for interactive pharmacophore exploration: extraction of pharmacophores activity delta Open
This paper presents a novel approach called Pharmacophore Activity Delta for extracting outstanding pharmacophores from a chemogenomic dataset, with a specific focus on a kinase target known as BCR-ABL. The method involves constructing a H…
View article: Towards a Partial Order Graph for Interactive Pharmacophore Exploration: Extraction of Pharmacophores Activity Delta
Towards a Partial Order Graph for Interactive Pharmacophore Exploration: Extraction of Pharmacophores Activity Delta Open
This paper describes an original approach for extracting outstanding pharmacophores, named PADs (for Pharmacophore Activity Delta), from a chemogenomic dataset (BCR-ABL in our case). This involves building both a partial order graph (POG) …
View article: NetPrune: A sparklines visualization for network pruning
NetPrune: A sparklines visualization for network pruning Open
Current deep learning approaches are cutting-edge methods for solving classification tasks. Arising transfer learning techniques allows applying large generic model to simple tasks whereas simpler models could be used. Large models raise t…
View article: Faster Edge‐Path Bundling through Graph Spanners
Faster Edge‐Path Bundling through Graph Spanners Open
Edge‐Path bundling is a recent edge bundling approach that does not incur ambiguities caused by bundling disconnected edges together. Although the approach produces less ambiguous bundlings, it suffers from high computational cost. In this…
View article: State of the Art of Visual Analytics for eXplainable Deep Learning
State of the Art of Visual Analytics for eXplainable Deep Learning Open
The use and creation of machine‐learning‐based solutions to solve problems or reduce their computational costs are becoming increasingly widespread in many domains. Deep Learning plays a large part in this growth. However, it has drawbacks…
View article: Toward Efficient Deep Learning for Graph Drawing (DL4GD)
Toward Efficient Deep Learning for Graph Drawing (DL4GD) Open
Due to their great performance in many challenges, Deep Learning (DL) techniques keep gaining popularity in many fields. They have been adapted to process graph data structures to solve various complicated tasks such as graph classificatio…
View article: Big data visualization and analytics: Future research challenges and emerging applications
Big data visualization and analytics: Future research challenges and emerging applications Open
© 2020 Copyright for this paper by its author(s). In the context of data visualization and analytics, this report outlines some of the challenges and emerging applications that arise in the Big Data era. In particularly, fourteen distingui…
View article: Relative Confusion Matrix: Efficient Comparison of Decision Models
Relative Confusion Matrix: Efficient Comparison of Decision Models Open
International audience
View article: VRGrid: Efficient Transformation of 2D Data into Pixel Grid Layout
VRGrid: Efficient Transformation of 2D Data into Pixel Grid Layout Open
International audience
View article: Edge-Path Bundling: A Less Ambiguous Edge Bundling Approach
Edge-Path Bundling: A Less Ambiguous Edge Bundling Approach Open
Edge bundling techniques cluster edges with similar attributes (i.e. similarity in direction and proximity) together to reduce the visual clutter. All edge bundling techniques to date implicitly or explicitly cluster groups of individual e…
View article: Deep Neural Network for DrawiNg Networks, (DNN)^2
Deep Neural Network for DrawiNg Networks, (DNN)^2 Open
By leveraging recent progress of stochastic gradient descent methods, several works have shown that graphs could be efficiently laid out through the optimization of a tailored objective function. In the meantime, Deep Learning (DL) techniq…
View article: Analysis of Deep Neural Networks Correlations with Human Subjects on a Perception Task
Analysis of Deep Neural Networks Correlations with Human Subjects on a Perception Task Open
International audience
View article: Impacts of the Numbers of Colors and Shapes on Outlier Detection: from Automated to User Evaluation
Impacts of the Numbers of Colors and Shapes on Outlier Detection: from Automated to User Evaluation Open
The design of efficient representations is well established as a fruitful way to explore and analyze complex or large data. In these representations, data are encoded with various visual attributes depending on the needs of the representat…
View article: Graph Drawing and Network Visualization GD2020
Graph Drawing and Network Visualization GD2020 Open
Proceedings of GD2020: This volume contains the papers presented at GD~2020, the 28th International Symposium on Graph Drawing and Network Visualization, held on September 18-20, 2020 online. Graph drawing is concerned with the geometric r…
View article: Deep Dive into Deep Neural Networks with Flows
Deep Dive into Deep Neural Networks with Flows Open
International audience
View article: Deep Dive into Deep Neural Networks with Flows
Deep Dive into Deep Neural Networks with Flows Open
Deep neural networks are becoming omnipresent in reason of their growing popularity in media and their daily use. However, their global complexity makes them hard to understand which emphasizes their black-box aspect and the lack of confid…
View article: CorFish: Coordinating Emphasis across Multiple Views using Spatial Distortion
CorFish: Coordinating Emphasis across Multiple Views using Spatial Distortion Open
International audience
View article: Cornac: Tackling Huge Graph Visualization with Big Data Infrastructure
Cornac: Tackling Huge Graph Visualization with Big Data Infrastructure Open
International audience
View article: Data-Oriented Algorithm for Real-Time Estimation of Flow Rates and Flow\n Directions in a Water Distribution Network
Data-Oriented Algorithm for Real-Time Estimation of Flow Rates and Flow\n Directions in a Water Distribution Network Open
The aim of this paper is to present how data collected from a water\ndistribution network (WDN) can be used to reconstruct flow rate and flow\ndirection all over the network to enhance knowledge and detection of unforeseen\nevents. The met…
View article: Data-Oriented Algorithm for Real-Time Estimation of Flow Rates and Flow Directions in a Water Distribution Network
Data-Oriented Algorithm for Real-Time Estimation of Flow Rates and Flow Directions in a Water Distribution Network Open
The aim of this paper is to present how data collected from a water distribution network (WDN) can be used to reconstruct flow rate and flow direction all over the network to enhance knowledge and detection of unforeseen events. The method…
View article: Enabling Hierarchical Exploration for Large-Scale Multidimensional Data with Abstract Parallel Coordinates
Enabling Hierarchical Exploration for Large-Scale Multidimensional Data with Abstract Parallel Coordinates Open
International audience
View article: IndexMEED cases studies using "Omics" data with graph theory
IndexMEED cases studies using "Omics" data with graph theory Open
International audience
View article: MuGDAD: Dessin de graphe multiéchelle en environnement distribué
MuGDAD: Dessin de graphe multiéchelle en environnement distribué Open
International audience