Sebastian Caldas

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 56 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #10,858 of 35,345 by trust here, #5,120 by citations
• Federated & Distributed Learning — 30 citations into it; #112 of 319 by trust here, #52 by citations
• Optimization & Training — 21 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Computational Mechanics — 16 citations into it
• Computer Networks and Communications — 14 citations into it
• Efficiency & Compression — 8 citations into it; #1,038 of 1,264 by trust here, #731 by citations

Whom Sebastian Caldas trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Virginia Smith
5. Yoshua Bengio
6. H. Brendan McMahan
7. Jakub Konecný
8. Diederik P. Kingma
9. Ilya Sutskever
10. Ruslan Salakhutdinov

Who trusts Sebastian Caldas overall

1. Tian Li
2. Zachary B. Charles
3. Chaoyang He
4. Peter Kairouz
5. Jakub Konecný
6. Othmane Marfoq
7. Tzu-Ming Harry Hsu
8. Yihan Jiang
9. Percy Liang
10. Qinbin Li

Papers from Sebastian Caldas's perspective

Ordered by how strongly the researchers Sebastian Caldas 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. Gradient-based learning applied to document recognition
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. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• LEAF: A Benchmark for Federated Settings (2018) — 1,782 citations
• Expanding the Reach of Federated Learning by Reducing Client Resource Requirements (2018) — 524 citations
• Differentially Private Meta-Learning (2019) — 127 citations
• Federated Kernelized Multi-Task Learning (2018)