Ian Fischer
- Artificial Intelligence top 5%
- Signal Processing top 10%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Control and Systems Engineering
- Co-authors
- Shumeet BalujaRachna DhamijaStuart SchechterAndy OzmentCraig GotsmanDavid HaDanijar HafnerTimothy Lillicrap
- Topics
- Adversarial Robustness in Machine Learning (3 papers)Digital Image Processing Techniques (2 papers)Machine Learning and Algorithms (2 papers)
- Journals
- Nature BiotechnologyEntropyarXiv (Cornell University)
- Partner nations
- United StatesAustriaGermany
In The Last Decade
Ian Fischer
9 papers receiving 326 citations
Peers
Comparison fields: 5 of 51
- Artificial Intelligence 205
- Signal Processing 99
- Computer Vision and Pattern Recognition 96
- Information Systems 85
- Control and Systems Engineering 41
Countries citing papers authored by Ian Fischer
This map shows the geographic impact of Ian Fischer'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 Ian Fischer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ian Fischer more than expected).
Fields of papers citing papers by Ian Fischer
This network shows the impact of papers produced by Ian Fischer. 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 Ian Fischer. The network helps show where Ian Fischer may publish in the future.
Co-authorship network of co-authors of Ian Fischer
This figure shows the co-authorship network connecting the top 25 collaborators of Ian Fischer. A scholar is included among the top collaborators of Ian Fischer 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 Ian Fischer. Ian Fischer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | An information-theoretic analysis of deep latent-variable models | 10 |
| 4 | 99 | |
| 5 | 109 | |
| 6 | The Emperor's New Security Indicators An evaluation of website authentication and the effect of role playing on usability studies † | 86 |
| 7 | 1 | |
| 8 | 41 | |
| 9 | 1 | |
| 10 | Application of the principle of transference to the evaluation of translational displacements in spatial mechanisms | 1 |
About Ian Fischer
Ian Fischer is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 10 papers that have together received 351 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Digital Image Processing Techniques (2 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Signal Processing (99 citations), Computer Graphics and Computer-Aided Design (30 citations) and Artificial Intelligence (205 citations). Ian Fischer has collaborated with scholars based in United States, Austria and Germany. Frequent co-authors include Shumeet Baluja, Rachna Dhamija, Stuart Schechter, Andy Ozment, Craig Gotsman, David Ha, Danijar Hafner, Timothy Lillicrap, James Davidson and Ruben Villegas. Their work appears in journals such as Nature Biotechnology, Entropy and arXiv (Cornell University).
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.