Cédric Renggli

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 147 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it; #12,171 of 35,345 by trust here, #10,207 by citations
• Computer Vision and Pattern Recognition — 83 citations into it
• Optimization & Training — 67 citations into it; #1,916 of 2,601 by trust here, #1,280 by citations
• Representation & Self-Supervised Learning — 32 citations into it
• Computer Networks and Communications — 31 citations into it
• ML Systems & Hardware — 28 citations into it
• Computational Mechanics — 23 citations into it; #7,303 of 10,001 by trust here, #2,942 by citations
• Model Architectures — 17 citations into it

Whom Cédric Renggli trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Stephen J. Wright
5. Nikko Ström
6. Rolf Rabenseifner
7. X. Zhang
8. Yoshua Bengio
9. Yann LeCun
10. Ross B. Girshick

Who trusts Cédric Renggli overall

1. Hanlin Tang
2. Tal Ben-Nun
3. Zhenheng Tang
4. Xiangru Lian
5. Torsten Hoefler
6. Shaohuai Shi
7. Tong Zhang
8. Mohammad Mohammadi Amiri
9. Ji Liu
10. Sebastian U. Stich

Papers from Cédric Renggli's perspective

Ordered by how strongly the researchers Cédric Renggli 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Scalable Transfer Learning with Expert Models (2020) — 72 citations
• The Convergence of Sparsified Gradient Methods (2018) — 553 citations
• Distributed Learning over Unreliable Networks (2018) — 67 citations
• SparCML: High-Performance Sparse Communication for Machine Learning (2018) — 149 citations