Andreas Ruckl'e

Artificial Intelligence · 7 papers · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 343 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 262 citations into it; #6,224 of 35,345 by trust here, #3,009 by citations
• Natural Language Processing — 211 citations into it; #2,895 of 5,688 by trust here, #2,440 by citations
• Computer Vision and Pattern Recognition — 110 citations into it; #5,054 of 21,575 by trust here, #2,332 by citations
• Evaluation & Benchmarks — 70 citations into it; #2,731 of 3,147 by trust here, #888 by citations
• Representation & Self-Supervised Learning — 61 citations into it; #1,203 of 3,145 by trust here, #1,138 by citations
• Information Retrieval & Search — 43 citations into it; #929 of 1,022 by trust here, #170 by citations
• Large Language Models — 34 citations into it
• Efficiency & Compression — 24 citations into it

Whom Andreas Ruckl'e trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Jonas Pfeiffer
5. Tomas Mikolov
6. Ilya Sutskever
7. Yann LeCun
8. Alex Krizhevsky
9. Kaiming He
10. Richard Socher

Who trusts Andreas Ruckl'e overall

1. Jonas Pfeiffer
2. Clifton A. Poth
3. Iryna Gurevych
4. Phillip Rust
5. Ivan Vulic
6. Yanzhe Zhang
7. B. Ermiş
8. Sebastian Ruder
9. Ruidan He
10. Xuechen Li

Papers from Andreas Ruckl'e's perspective

Ordered by how strongly the researchers Andreas Ruckl'e trusts endorse each paper. Same corpus, a different order for every researcher.

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

• BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models (2021) — 1,831 citations
• AdapterFusion: Non-Destructive Task Composition for Transfer Learning (2020) — 1,208 citations
• AdapterHub: A Framework for Adapting Transformers (2020) — 753 citations
• What to Pre-Train on? Efficient Intermediate Task Selection (2021) — 108 citations
• AdapterDrop: On the Efficiency of Adapters in Transformers (2020) — 324 citations
• Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations (2018) — 102 citations
• MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale (2020) — 39 citations