J. Perez

Artificial Intelligence · with Brun · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 422 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #15,157 of 35,345 by trust here, #10,430 by citations
• Computer Vision and Pattern Recognition — 65 citations into it; #15,471 of 21,575 by trust here, #10,010 by citations
• Natural Language Processing — 36 citations into it
• Representation & Self-Supervised Learning — 33 citations into it; #2,405 of 3,145 by trust here, #1,843 by citations
• Evaluation & Benchmarks — 21 citations into it
• Multimodal Learning — 14 citations into it; #823 of 1,211 by trust here, #770 by citations
• Model Architectures — 6 citations into it
• Optimization & Training — 5 citations into it

Whom J. Perez trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Yoshua Bengio
5. Karen Simonyan
6. Alex Graves
7. Diederik P. Kingma
8. Ross B. Girshick
9. Ilya Sutskever
10. Yann LeCun

Who trusts J. Perez overall

1. Karan Desai
2. Puyang Xu
3. Federico Bianchi
4. Yiwu Zhong
5. Nikola Mrksic
6. Benjamin Devillers
7. Justin Johnson
8. Hieu Pham
9. Menglin Jia
10. Philip Müller

Papers from J. Perez's perspective

Ordered by how strongly the researchers J. Perez 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning Visual Representations with Caption Annotations (2020) — 173 citations
• Motivating Personality-aware Machine Translation (2015) — 58 citations
• Dialog state tracking, a machine reading approach using Memory Network (2016) — 59 citations