Sebastian Hofstätter

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 12 papers; trusts 349 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 240 citations into it; #3,989 of 35,345 by trust here, #6,468 by citations
• Information Retrieval & Search — 153 citations into it; #149 of 1,022 by trust here, #241 by citations
• Natural Language Processing — 91 citations into it
• Computer Vision and Pattern Recognition — 87 citations into it; #11,793 of 21,575 by trust here, #8,952 by citations
• Information Systems — 71 citations into it; #4,606 of 8,771 by trust here, #5,942 by citations
• Evaluation & Benchmarks — 46 citations into it; #1,217 of 3,147 by trust here, #2,671 by citations
• Model Architectures — 37 citations into it
• Large Language Models — 36 citations into it

Whom Sebastian Hofstätter trusts overall

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

1. Bhaskar Mitra
2. Diederik P. Kingma
3. Rodrigo Nogueira
4. Nick Craswell
5. Geoffrey E. Hinton
6. Jacob Devlin
7. Kyunghyun Cho
8. Yoshua Bengio
9. Tomas Mikolov
10. Zhuyun Dai

Who trusts Sebastian Hofstätter overall

1. Jimmy J. Lin
2. Sheng-Chieh Lin
3. Omar Khattab
4. Allan Hanbury
5. Jyun-Yu Jiang
6. Mete Sertkan
7. Jheng-Hong Yang
8. Bhaskar Mitra
9. Sophia Althammer
10. Rodrigo Nogueira

Papers from Sebastian Hofstätter's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling (2021) — 521 citations
• Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling (2022) — 14 citations
• Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation (2020) — 290 citations
• Let's measure run time! Extending the IR replicability infrastructure to include performance aspects (2019) — 48 citations
• TU Wien @ TREC Deep Learning '19 - Simple Contextualization for Re-ranking (2019) — 15 citations
• Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models (2024) — 271 citations
• Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction (2022) — 54 citations
• TU Wien at TREC DL and Podcast 2021: Simple Compression for Dense Retrieval (2021)
• Conformer-Kernel with Query Term Independence for Document Retrieval (2020) — 22 citations
• Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking (2020) — 84 citations
• Local Self-Attention over Long Text for Efficient Document Retrieval (2020) — 96 citations
• On the Effect of Low-Frequency Terms on Neural-IR Models (2019) — 41 citations