Samuel Broscheit

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 125 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #14,351 of 35,345 by trust here, #16,771 by citations
• Natural Language Processing — 86 citations into it; #2,754 of 5,688 by trust here, #3,478 by citations
• Computer Vision and Pattern Recognition — 51 citations into it
• Information Retrieval & Search — 43 citations into it
• Evaluation & Benchmarks — 32 citations into it
• Information Systems — 21 citations into it
• Large Language Models — 13 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Samuel Broscheit trusts overall

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

1. Diederik P. Kingma
2. Ikuya Yamada
3. Jimmy Ba
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Ilya Sutskever
7. Jacob Devlin
8. Ashish Vaswani
9. Alex Krizhevsky
10. Matthew E. Peters

Who trusts Samuel Broscheit overall

1. Nicola De Cao
2. Gautier Izacard
3. Tara Safavi
4. Fabio Petroni
5. Sebastian Riedel
6. Nina Poerner
7. Rajarshi Das
8. Luca Massarelli

Papers from Samuel Broscheit's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. 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

• The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus (2021) — 79 citations
• Improving Wikipedia verifiability with AI (2022) — 23 citations
• Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking (2019) — 138 citations
• A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval (2018) — 12 citations