Katja Hose

Artificial Intelligence · 2011–2019

A researcher in the Science corpus: 4 papers; trusts 19 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #16,616 of 35,345 by trust here, #13,353 by citations
• Computer Networks and Communications — 14 citations into it; #9,068 of 9,886 by trust here, #9,214 by citations
• Information Systems — 13 citations into it; #3,796 of 8,771 by trust here, #2,406 by citations
• Signal Processing — 11 citations into it; #7,055 of 7,572 by trust here, #7,112 by citations
• Information Retrieval & Search — 9 citations into it
• Natural Language Processing — 8 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Katja Hose trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Fabian M. Suchanek
2. Stephen H. Muggleton
3. Matthew Richardson
4. Natasha Noy
5. Rakesh Agrawal
6. Gerhard Weikum
7. Tomas Mikolov
8. Christian Bizer
9. Deborah L. McGuinness
10. Quoc V. Le

Who trusts Katja Hose overall

1. Quan Wang
2. Sarasi Lalithsena
3. Andrea Rossi
4. Naser Ahmadi

Papers from Katja Hose's perspective

Ordered by how strongly the researchers Katja Hose trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. GloVe: Global Vectors for Word Representation
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• AMIE: association rule mining under incomplete evidence in ontological knowledge bases (2013) — 770 citations
• Retrieving Textual Evidence for Knowledge Graph Facts (2019) — 13 citations
• Fast rule mining in ontological knowledge bases with AMIE+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{docu (2015) — 560 citations
• FedX: Optimization Techniques for Federated Query Processing on Linked Data (2011) — 390 citations