Maxime Kayser

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 34 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #31,647 of 35,345 by trust here, #26,682 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #19,463 of 21,575 by trust here, #17,258 by citations
• Natural Language Processing — 21 citations into it
• Evaluation & Benchmarks — 12 citations into it; #2,710 of 3,147 by trust here, #2,799 by citations

Whom Maxime Kayser trusts overall

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

1. Kaiming He
2. Chin-Yew Lin
3. Geoffrey E. Hinton
4. Shaoqing Ren
5. Alex Krizhevsky
6. R. Caruana
7. Yoshua Bengio
8. Diederik P. Kingma
9. Ilya Sutskever
10. Ross B. Girshick

Who trusts Maxime Kayser overall

1. Bodhisattwa Prasad Majumder
2. Oana-Maria Camburu
3. Ana Marasovic
4. Thomas Lukasiewicz
5. Yordan Yordanov
6. Yaru Hao
7. Sarah Wiegreffe

Papers from Maxime Kayser's perspective

Ordered by how strongly the researchers Maxime Kayser 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks (2021) — 118 citations