Timothy Niven

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 63 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #9,142 of 35,345 by trust here, #9,968 by citations
• Natural Language Processing — 33 citations into it; #1,869 of 5,688 by trust here, #2,203 by citations
• Evaluation & Benchmarks — 11 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #19,571 of 21,575 by trust here, #21,424 by citations
• Model Architectures — 7 citations into it
• Optimization & Training — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Timothy Niven trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Sepp Hochreiter
4. Ivan Habernal
5. Samuel R. Bowman
6. Yoshua Bengio
7. Jimmy Ba
8. J. Urgen Schmidhuber
9. Alex Krizhevsky
10. Nitish Srivastava

Who trusts Timothy Niven overall

1. Samuel R. Bowman
2. Chadi Helwe
3. Nicholas Lourie
4. Pengcheng He
5. Alexandre Tamborrino
6. Emily M. Bender
7. Paul Röttger
8. Danish Contractor
9. Hung-Yu kao
10. John Lawrence

Papers from Timothy Niven's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Probing Neural Network Comprehension of Natural Language Arguments (2019) — 498 citations
• Fill the GAP: Exploiting BERT for Pronoun Resolution (2019) — 4 citations
• NLITrans at SemEval-2018 Task 12: Transfer of Semantic Knowledge for Argument Comprehension (2018) — 4 citations