Jérémie Mary

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 103 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #18,563 of 35,345 by trust here, #6,956 by citations
• Computer Vision and Pattern Recognition — 35 citations into it; #6,786 of 21,575 by trust here, #5,013 by citations
• Multimodal Learning — 18 citations into it; #707 of 1,211 by trust here, #526 by citations
• Reinforcement Learning — 11 citations into it
• Information Systems — 8 citations into it
• Model Architectures — 8 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Jérémie Mary trusts overall

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

1. Geoffrey E. Hinton
2. Ruslan Salakhutdinov
3. Yoshua Bengio
4. Kaiming He
5. Yann LeCun
6. Steffen Rendle
7. Mark A. Kramer
8. Ronald J. Williams
9. Alex Krizhevsky
10. Diederik P. Kingma

Who trusts Jérémie Mary overall

1. Takeru Miyato
2. Xiangnan He
3. Shuai Zhang
4. Zihan Lin
5. Zeynep Batmaz
6. Haonan Yu
7. Tsendsuren Munkhdalai
8. Ming Tao

Papers from Jérémie Mary's perspective

Ordered by how strongly the researchers Jérémie Mary 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Modulating early visual processing by language (2017) — 551 citations
• End-to-end optimization of goal-driven and visually grounded dialogue systems (2017) — 141 citations
• Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs (2015) — 169 citations