Jonathan Mallinson

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 5 papers; trusts 288 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 139 citations into it; #8,157 of 35,345 by trust here, #10,065 by citations
• Natural Language Processing — 132 citations into it; #1,455 of 5,688 by trust here, #2,529 by citations
• Computer Vision and Pattern Recognition — 59 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Large Language Models — 25 citations into it
• Model Architectures — 21 citations into it
• Representation & Self-Supervised Learning — 12 citations into it; #1,528 of 3,145 by trust here, #2,454 by citations
• Machine Translation — 6 citations into it; #82 of 292 by trust here, #150 by citations

Whom Jonathan Mallinson trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ilya Sutskever
5. Kyunghyun Cho
6. Oriol Vinyals
7. Philipp Koehn
8. Alex Krizhevsky
9. Rico Sennrich
10. Alex Graves

Who trusts Jonathan Mallinson overall

1. John Wieting
2. Kevin Gimpel
3. Mohit Iyyer
4. Felix Stahlberg
5. Shrimai Prabhumoye
6. Rahul Aralikatte
7. Markus Freitag
8. Wanyu Du
9. Brian Thompson
10. Jun Quan

Papers from Jonathan Mallinson's perspective

Ordered by how strongly the researchers Jonathan Mallinson 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. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Gradient-based learning applied to document recognition
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Learning to Paraphrase for Question Answering (2017) — 225 citations
• Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext (2017) — 111 citations
• Teaching Small Language Models to Reason (2022) — 417 citations
• FELIX: Flexible Text Editing Through Tagging and Insertion (2020) — 89 citations
• Paraphrasing Revisited with Neural Machine Translation (2017) — 224 citations