Noé Casas

480 citations
16 papers · 66 · h-index 5

Impact in

Papers in

Noé Casas

16 papers receiving 63 citations

Peers

Noé Casas
Comparison fields: 5 of 33
  • Artificial Intelligence 46
  • Computer Vision and Pattern Recognition 19
  • Health Informatics 1
  • Human-Computer Interaction 3
  • Ophthalmology 5
Replace Aku Rouhe with:
Aku Rouhe Finland
Julia Kreutzer Germany
Shariq Iqbal United States
Maha Elbayad United States
Khyathi Raghavi Chandu United States
Quang Pham Singapore
Vikas Yadav United States
Gihun Lee South Korea
Kaizhao Liang United States
Davide Testuggine United States
Noé Casas relative to Aku Rouhe Finland Aku Rouhe's profile →
Citations per field
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Citations per year

Countries citing papers authored by Noé Casas

Since Specialization
Citations

This map shows the geographic impact of Noé Casas'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 Noé Casas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Noé Casas more than expected).

Fields of papers citing papers by Noé Casas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Noé Casas. 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 Noé Casas. The network helps show where Noé Casas may publish in the future.

Co-authors

The 9 scholars most cited alongside Noé Casas, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Noé Casas Line = papers co-authored together Noé Casas links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 201910
2 20209
3 20198
4 20156
5 20205
6
A differentiable BLEU loss. Analysis and first results.
20184
7 20194
8
Deep reinforcement learning for urban traffic light control
20174
9
English-catalan neural machine translation in the biomedical domain through the cascade approach
20183
10 20223
11 20202
12 20202
13 20222
14 20202
15 20181
16 20151

About Noé Casas

Noé Casas is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging and Control and Systems Engineering, having authored 16 papers that have together received 66 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (10 papers), Topic Modeling (9 papers), Text Readability and Simplification (3 papers), Glaucoma and retinal disorders (2 papers), Evolutionary Algorithms and Applications (2 papers), Multimodal Machine Learning Applications (2 papers), Retinal Imaging and Analysis (2 papers) and Metaheuristic Optimization Algorithms Research (2 papers). The work is most often cited by research in Artificial Intelligence (46 citations), Computer Vision and Pattern Recognition (19 citations), Health Informatics (1 citation), Human-Computer Interaction (3 citations) and Ophthalmology (5 citations). Noé Casas has collaborated with scholars based in Spain and United States. Frequent co-authors include Marta R. Costa‐jussà, José A. R. Fonollosa, Carlos Escolano, J. A. Pajares, Bharathi Raja Chakravarthi, Mihael Arčan, Gorka Labaka, Mikel Artetxe and Eneko Agirre. Their work appears in journals such as Natural Language Engineering, Neural Computing and Applications, ACM Transactions on Asian and Low-Resource Language Information Processing, Archivos de la Sociedad Española de Oftalmología and International Conference on Learning Representations.

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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