Jean-Marc Andreoli

Artificial Intelligence · 2019

A researcher in the Science corpus: 3 papers; trusts 101 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 28 citations into it; #9,877 of 35,345 by trust here, #19,873 by citations
• Model Architectures — 16 citations into it; #956 of 2,278 by trust here, #1,882 by citations
• Computer Vision and Pattern Recognition — 14 citations into it
• Natural Language Processing — 14 citations into it
• Reinforcement Learning — 11 citations into it; #1,240 of 2,242 by trust here, #2,063 by citations
• Probabilistic & Bayesian Methods — 6 citations into it
• Graph Learning — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Jean-Marc Andreoli trusts overall

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

1. Yann LeCun
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ilya Sutskever
6. Diederik P. Kingma
7. Sepp Hochreiter
8. Ronald J. Williams
9. Ashish Vaswani
10. Oriol Vinyals

Who trusts Jean-Marc Andreoli overall

1. Tomasz Korbak
2. Bryan Eikema
3. Hady ElSahar
4. Marc Dymetman
5. Germán Kruszewski
6. Yuntian Deng
7. Anton Bakhtin
8. Jean-Baptiste Cordonnier
9. Tetiana Parshakova
10. Huajun Liu

Papers from Jean-Marc Andreoli's perspective

Ordered by how strongly the researchers Jean-Marc Andreoli trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Global Autoregressive Models for Data-Efficient Sequence Learning (2019) — 23 citations
• Distributional Reinforcement Learning for Energy-Based Sequential Models (2019) — 23 citations
• Convolution, attention and structure embedding. (2019) — 27 citations