CsAuthors.net database
Most of the data is coming from the
DBLP Computer Science Bibliography
and the rest is coming from CsAuthors.net own database.
We are working hard to keep everything up-to-date. However, we know that there are many papers not yet included in our dataset.
If something is wrong or missing, feel free to write me at
We are working hard to keep everything up-to-date. However, we know that there are many papers not yet included in our dataset.
If something is wrong or missing, feel free to write me at
my email address
.
The "Dijkstra number"
The Dijkstra number describes the collaborative distance between an author and
Edsger W. Dijkstra.
In our dataset 90.4% of authors are connected to Edsger W. Dijkstra and the average Dijkstra number among them is 5.05.
These kind of number/metrics are quite famous and already well defined in other fields.
In our dataset 90.4% of authors are connected to Edsger W. Dijkstra and the average Dijkstra number among them is 5.05.
These kind of number/metrics are quite famous and already well defined in other fields.
- The "Erdős number" expresses the collaborative distance with Paul Erdős, the famous Hungarian mathematician.
- The "Bacon number" expresses the co-acting distance with Kevin Bacon.
The "Erdős number"
The Erdős number describes the collaborative distance between an author and
Paul Erdős.
In our dataset 90.4% of authors are connected to Paul Erdős and the average Erdős number among them is 4.64.
Find more on Wikipedia with an article on the"Erdős number".
In our dataset 90.4% of authors are connected to Paul Erdős and the average Erdős number among them is 4.64.
Find more on Wikipedia with an article on the"Erdős number".
Jackson Kernion
According to our database1,
Jackson Kernion
authored at least 15 papers
between 2021 and 2023.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2023
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.
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Trans. Mach. Learn. Res., 2023
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CoRR, 2023
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CoRR, 2023
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CoRR, 2023
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CoRR, 2023
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Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023
2022
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CoRR, 2022
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CoRR, 2022
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CoRR, 2022
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CoRR, 2022
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.
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CoRR, 2022
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CoRR, 2022
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.
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CoRR, 2022
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CoRR, 2022
2021
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CoRR, 2021