This map shows the geographic impact of Hannes Schulz'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 Hannes Schulz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hannes Schulz more than expected).
This network shows the impact of papers produced by Hannes Schulz. 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 Hannes Schulz. The network helps show where Hannes Schulz may publish in the future.
Co-authorship network of co-authors of Hannes Schulz
This figure shows the co-authorship network connecting the top 25 collaborators of Hannes Schulz.
A scholar is included among the top collaborators of Hannes Schulz 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 Hannes Schulz. Hannes Schulz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Li, Jinchao, Baolin Peng, Sung‐Jin Lee, et al.. (2020). Results of the Multi-Domain Task-Completion Dialog Challenge. National Conference on Artificial Intelligence.13 indexed citations
3.
Nguyen, Dat Tien, Shikhar Sharma, Hannes Schulz, & Layla El Asri. (2019). From FiLM to Video: Multi-turn Question Answering with Multi-modal Context. National Conference on Artificial Intelligence.5 indexed citations
4.
El-Nouby, Alaaeldin, Shikhar Sharma, Hannes Schulz, et al.. (2018). Keep Drawing It: Iterative language-based image generation and editing.. arXiv (Cornell University).4 indexed citations
5.
Sharma, Shikhar, Jing He, Kaheer Suleman, Hannes Schulz, & Philip Bachman. (2017). Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data. arXiv (Cornell University).3 indexed citations
Schulz, Hannes, et al.. (2015). Depth and Height Aware Semantic RGB-D Perception with Convolutional Neural Networks. The European Symposium on Artificial Neural Networks.7 indexed citations
Schulz, Hannes & Sven Behnke. (2012). Learning Object-Class Segmentation with Convolutional Neural Networks. The European Symposium on Artificial Neural Networks.26 indexed citations
12.
Schulz, Hannes & Sven Behnke. (2012). Deep Learning. KI - Künstliche Intelligenz. 26(4). 357–363.53 indexed citations
Schulz, Hannes, Andreas Müller, & Sven Behnke. (2010). Investigating Convergence of Restricted Boltzmann Machine Learning. Neural Information Processing Systems.18 indexed citations
15.
Schulz, Hannes & Sven Behnke. (2010). Exploiting Local Structure in Stacked Boltzmann Machines. The European Symposium on Artificial Neural Networks.2 indexed citations
Jørgensen, Lars Nannestad, Lars Bödker, & Hannes Schulz. (1990). Validation of the threshold for eyespot (Pseudocercosporella herpotrichoides) in winter wheat and winter rye assessed in spring and July.. 94(2). 223–232.1 indexed citations
19.
Bödker, Lars, Hannes Schulz, & Kurt Kristensen. (1990). Influence of cultural practices on incidence of take-all (Gaeumannomyces graminis var. tritici) in winter wheat and winter rye.. 94(2). 201–209.4 indexed citations
20.
Schulz, Hannes, Lars Bödker, Lars Nannestad Jørgensen, & Kurt Kristensen. (1990). Influence of different cultural practices on distribution and incidence of eyespot (Pseudocercosporella herpotrichoides) in winter rye and winter wheat.. 94(2). 211–221.7 indexed citations
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.