Markus Zlabinger

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 77 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #11,730 of 35,345 by trust here, #23,731 by citations
• Information Retrieval & Search — 21 citations into it; #448 of 1,022 by trust here, #821 by citations
• Natural Language Processing — 17 citations into it
• Information Systems — 14 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Large Language Models — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Markus Zlabinger trusts overall

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

1. Diederik P. Kingma
2. Bhaskar Mitra
3. Geoffrey E. Hinton
4. Jimmy Ba
5. Jacob Devlin
6. Rodrigo Nogueira
7. Yoshua Bengio
8. Jeffrey Pennington
9. Kai Hui
10. Richard Socher

Who trusts Markus Zlabinger overall

1. Sebastian Hofstätter
2. Jyun-Yu Jiang
3. Bhaskar Mitra
4. Sean MacAvaney
5. Chenyan Xiong
6. Mete Sertkan
7. Allan Hanbury
8. Hamed Zamani
9. Sophia Althammer

Papers from Markus Zlabinger's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• TU Wien @ TREC Deep Learning '19 - Simple Contextualization for Re-ranking (2019) — 15 citations
• Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking (2020) — 84 citations