Leila Arras

901 total citations
5 papers, 250 citations indexed

About

Leila Arras is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Leila Arras has authored 5 papers receiving a total of 250 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 1 paper in Health, Toxicology and Mutagenesis. Recurrent topics in Leila Arras's work include Explainable Artificial Intelligence (XAI) (3 papers), Advanced Neural Network Applications (2 papers) and Energy Load and Power Forecasting (1 paper). Leila Arras is often cited by papers focused on Explainable Artificial Intelligence (XAI) (3 papers), Advanced Neural Network Applications (2 papers) and Energy Load and Power Forecasting (1 paper). Leila Arras collaborates with scholars based in Germany, Denmark and South Korea. Leila Arras's co-authors include Wojciech Samek, Franziska Horn, Grégoire Montavon, Klaus‐Robert Müller, Sebastian Lapuschkin, Onyebuchi A. Arah, Andreas Rieckmann, Claus Thorn Ekstrøm, Piotr Dworzyński and Naja Hulvej Rod and has published in prestigious journals such as PLoS ONE, Scientific Reports and International Journal of Epidemiology.

In The Last Decade

Leila Arras

5 papers receiving 238 citations

Peers

Countries citing papers authored by Leila Arras

Since Specialization

Citations

This map shows the geographic impact of Leila Arras's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Leila Arras with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leila Arras more than expected).

Fields of papers citing papers by Leila Arras

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Leila Arras. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Leila Arras. The network helps show where Leila Arras may publish in the future.

Co-authorship network of co-authors of Leila Arras

This figure shows the co-authorship network connecting the top 25 collaborators of Leila Arras. A scholar is included among the top collaborators of Leila Arras based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Leila Arras. Leila Arras is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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5 of 5 papers shown

#	Work	Indexed citations
1	Explainable sequence-to-sequence GRU neural network for pollution forecasting 2023 ·Scientific Reports ·(unknown), Leila Arras, (unknown), Wojciech Samek	19
2	Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome 2022 ·International Journal of Epidemiology ·Andreas Rieckmann, Piotr Dworzyński, Leila Arras, Sebastian Lapuschkin, Wojciech Samek, Onyebuchi A. Arah, Naja Hulvej Rod, Claus Thorn Ekstrøm	7
3	CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations 2021 ·Information Fusion ·Leila Arras, (unknown), Wojciech Samek	67
4	Towards Ground Truth Evaluation of Visual Explanations 2020 ·arXiv (Cornell University) ·(unknown), Leila Arras, Wojciech Samek	5
5	"What is relevant in a text document?": An interpretable machine learning approach 2017 ·PLoS ONE ·Leila Arras, Franziska Horn, Grégoire Montavon, Klaus‐Robert Müller, Wojciech Samek	152

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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