Youssef Mroueh

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 9 papers; trusts 551 researchers and is trusted by 324 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 146 citations into it; #2,472 of 35,345 by trust here, #3,204 by citations
• Computer Vision and Pattern Recognition — 125 citations into it; #2,256 of 21,575 by trust here, #2,485 by citations
• Computational Theory and Mathematics — 59 citations into it
• Generative Models — 58 citations into it; #436 of 1,956 by trust here, #922 by citations
• Materials Chemistry — 51 citations into it
• Molecular Biology — 48 citations into it
• Representation & Self-Supervised Learning — 40 citations into it
• Model Architectures — 37 citations into it

Whom Youssef Mroueh trusts overall

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

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

Who trusts Youssef Mroueh overall

1. Yongjun Hong
2. Uiwon Hwang
3. Alexia Jolicoeur-Martineau
4. Jiaming Song
5. Sung Woo Park
6. Abhishek Kumar
7. C. D. Santos
8. Marcella Cornia
9. Jaeyoon Yoo
10. Chun-Liang Li

Papers from Youssef Mroueh's perspective

Ordered by how strongly the researchers Youssef Mroueh 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Self-Critical Sequence Training for Image Captioning (2016) — 2,119 citations
• Fisher GAN (2017) — 134 citations
• McGan: Mean and Covariance Feature Matching GAN (2017) — 166 citations
• Learning Implicit Generative Models by Matching Perceptual Features (2019) — 32 citations
• Convex Learning of Multiple Tasks and their Structure (2015) — 70 citations
• Do Large Scale Molecular Language Representations Capture Important Structural Information? (2021) — 13 citations
• Large-scale chemical language representations capture molecular structure and properties (2021) — 591 citations
• Wasserstein Barycenter Model Ensembling (2019) — 27 citations
• Local Group Invariant Representations via Orbit Embeddings (2016) — 40 citations