Joachim Bingel

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 7 papers; trusts 130 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #4,888 of 35,345 by trust here, #7,797 by citations
• Natural Language Processing — 50 citations into it; #2,838 of 5,688 by trust here, #3,478 by citations
• Representation & Self-Supervised Learning — 38 citations into it; #3,055 of 3,145 by trust here, #2,863 by citations
• Computer Vision and Pattern Recognition — 35 citations into it; #4,634 of 21,575 by trust here, #7,249 by citations
• Safety, Robustness & Interpretability — 21 citations into it; #1,040 of 2,251 by trust here, #1,481 by citations
• Model Architectures — 20 citations into it; #1,472 of 2,278 by trust here, #1,620 by citations
• Optimization & Training — 12 citations into it; #1,871 of 2,601 by trust here, #1,585 by citations
• Computational Mechanics — 10 citations into it

Whom Joachim Bingel trusts overall

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

1. R. Caruana
2. Anders Søgaard
3. Kaiming He
4. Geoffrey E. Hinton
5. Barbara Plank
6. Yoshua Bengio
7. Jonathan Baxter
8. Alex Krizhevsky
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Joachim Bingel overall

1. Sebastian Ruder
2. Ramakanth Pasunuru
3. Han Guo
4. Isabelle Augenstein
5. Michael Crawshaw
6. Mohit Bansal
7. Victor Sanh
8. Yuan Gao
9. Jonas Pfeiffer
10. Zeerak Talat

Papers from Joachim Bingel's perspective

Ordered by how strongly the researchers Joachim Bingel 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Latent Multi-Task Architecture Learning (2017) — 414 citations
• Identifying beneficial task relations for multi-task learning in deep neural networks (2017) — 256 citations
• Disembodied Machine Learning: On the Illusion of Objectivity in NLP (2021) — 57 citations
• Bridging the Gaps: Multi Task Learning for Domain Transfer of Hate Speech Detection (2018) — 93 citations
• Sluice networks: Learning what to share between loosely related tasks (2017) — 177 citations
• Learning what to share between loosely related tasks (2017) — 41 citations
• Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016) — 147 citations