Daniel Beck

Artificial Intelligence · 2013–2018

A researcher in the Science corpus: 7 papers; trusts 79 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 58 citations into it; #11,010 of 35,345 by trust here, #11,921 by citations
• Natural Language Processing — 58 citations into it; #4,499 of 5,688 by trust here, #4,682 by citations
• Evaluation & Benchmarks — 33 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it; #18,783 of 21,575 by trust here, #20,271 by citations
• Graph Learning — 9 citations into it
• Machine Translation — 9 citations into it
• Computational Theory and Mathematics — 7 citations into it
• Molecular Biology — 7 citations into it; #4,533 of 17,773 by trust here, #9,367 by citations

Whom Daniel Beck trusts overall

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

1. Lucia Specia
2. Matthew G. Snover
3. Burr Settles
4. Trevor Cohn
5. Yoshua Bengio
6. Diederik P. Kingma
7. Geoffrey E. Hinton
8. Kashif Shah
9. Philipp Koehn
10. Ilya Sutskever

Who trusts Daniel Beck overall

1. Adyasha Maharana
2. Varvara Logacheva
3. Kashif Shah
4. Mohit Bansal
5. Marco Damonte
6. Mrinmaya Sachan
7. Luyang huang
8. Chao Zhao
9. Lucia Specia
10. Lingfei Wu

Papers from Daniel Beck's perspective

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

Their papers

• Graph-to-Sequence Learning using Gated Graph Neural Networks (2018) — 345 citations
• Word embeddings and discourse information for Quality Estimation (2016) — 11 citations
• SHEF-MIME: Word-level Quality Estimation Using Imitation Learning (2016) — 4 citations
• SHEF-NN: Translation Quality Estimation with Neural Networks (2015) — 25 citations
• SHEF-Lite 2.0: Sparse Multi-task Gaussian Processes for Translation Quality Estimation (2014) — 14 citations
• Reducing Annotation Effort for Quality Estimation via Active Learning (2013) — 9 citations
• SHEF-Lite: When Less is More for Translation Quality Estimation (2013) — 26 citations