Tobias Schnabel

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 5 papers; trusts 84 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #14,867 of 35,345 by trust here, #9,092 by citations
• Information Systems — 54 citations into it; #2,755 of 8,771 by trust here, #1,650 by citations
• Natural Language Processing — 42 citations into it
• Management Science and Operations Research — 33 citations into it; #8,404 of 11,946 by trust here, #3,357 by citations
• Evaluation & Benchmarks — 23 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Statistics and Probability — 15 citations into it; #9,625 of 12,932 by trust here, #4,517 by citations

Whom Tobias Schnabel trusts overall

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

1. Thorsten Joachims
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Tomas Mikolov
5. Ronan Collobert
6. Harald Steck
7. Diederik P. Kingma
8. Robert E. Schapire
9. Alex Krizhevsky
10. Ilya Sutskever

Who trusts Tobias Schnabel overall

1. Thorsten Joachims
2. Tal Linzen
3. Jinsung Yoon
4. Manaal Faruqui
5. Raghavendra Chalapathy
6. Anna Korhonen
7. Aman Agarwal
8. Stephen Bonner
9. Jiawei Chen
10. Neha Nayak Kennard

Papers from Tobias Schnabel's perspective

Ordered by how strongly the researchers Tobias Schnabel 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization (2021) — 574 citations
• Evaluation methods for unsupervised word embeddings (2015) — 583 citations
• Unbiased Learning-to-Rank with Biased Feedback (2016) — 656 citations
• Recommendations as Treatments: Debiasing Learning and Evaluation (2016) — 833 citations
• Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration (2018) — 53 citations