Jonathan Pilault

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 218 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #20,407 of 35,345 by trust here, #20,836 by citations
• Natural Language Processing — 76 citations into it; #3,461 of 5,688 by trust here, #4,032 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #15,260 of 21,575 by trust here, #19,310 by citations
• Evaluation & Benchmarks — 20 citations into it
• Large Language Models — 15 citations into it
• Model Architectures — 12 citations into it
• Optimization & Training — 11 citations into it
• Representation & Self-Supervised Learning — 10 citations into it; #2,382 of 3,145 by trust here, #2,957 by citations

Whom Jonathan Pilault trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Jimmy Ba
6. Alex Krizhevsky
7. Yann LeCun
8. Alex Graves
9. Sepp Hochreiter
10. Kaiming He

Who trusts Jonathan Pilault overall

1. Joe Stacey
2. Alexios Gidiotis
3. Yao Zhao
4. Weizhe Yuan
5. Tianyang Lin
6. Yulong Chen
7. Kundan Krishna
8. Yonatan Belinkov
9. Luyang Robby Huang
10. Zihao Fu

Papers from Jonathan Pilault's perspective

Ordered by how strongly the researchers Jonathan Pilault 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. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• On Extractive and Abstractive Neural Document Summarization with Transformer Language Models (2020) — 248 citations
• Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data (2020) — 105 citations