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David Antony Selby

German Research Center for Artificial Intelligence

7 papers hereh-index 8250 citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author1
  • middle author4

Across the 6 of 7 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.CE1
  • cs.IR1
  • q-bio.QM1
  • stat.ME1
affiliations
  • German Research Center for Artificial Intelligence
Homepage
same name
  • David Antony Selby — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedHad enough of experts? Quantitative knowledge retrieval from large language models

4 citations · 6 across the 7 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CE2024★ 2 cited

Rethinking Cancer Gene Identification through Graph Anomaly Analysis

Yilong Zang, Lingfei Ren, Yue Li +7

Graph neural networks (GNNs) have shown promise in integrating protein-protein interaction (PPI) networks for identifying cancer genes in recent studies. However, due to the insuff…

stat.ME2024

PageRank and the Bradley-Terry model

David Antony Selby

PageRank and the Bradley-Terry model are competing approaches to ranking entities such as teams in sports tournaments or journals in citation networks. The Bradley-Terry model is a…

cs.IR2024★ 4 cited

Had enough of experts? Quantitative knowledge retrieval from large language models

David Selby, Kai Spriestersbach, Yuichiro Iwashita +6

Large language models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences, however their utility for quantitative information…

cs.LG2024

X Hacking: The Threat of Misguided AutoML

Rahul Sharma, Sergey Redyuk, Sumantrak Mukherjee +4

Explainable AI (XAI) and interpretable machine learning methods help to build trust in model predictions and derived insights, yet also present a perverse incentive for analysts to…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.