Florian Schäfer

Artificial Intelligence · with Anandkumar · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 48 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #18,200 of 35,345 by trust here, #31,361 by citations
• Optimization & Training — 21 citations into it; #1,584 of 2,601 by trust here, #2,313 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Generative Models — 10 citations into it
• Computational Theory and Mathematics — 7 citations into it
• Management Science and Operations Research — 5 citations into it
• Reinforcement Learning — 5 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Florian Schäfer trusts overall

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

1. Yurii E. Nesterov
2. Jorge Nocedal
3. Y. Saad
4. David Pfau
5. Greg Wayne
6. Stephen J. Wright
7. Geoffrey E. Hinton
8. Francisco Facchinei
9. Alex Krizhevsky
10. David Balduzzi

Who trusts Florian Schäfer overall

1. Sifan Wang
2. Jeremy Bernstein
3. Yujun Teng
4. Arash Vahdat
5. Paris Perdikaris
6. Aravind Rajeswaran
7. Yisong Yue

Papers from Florian Schäfer's perspective

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

Their papers

• Competitive Gradient Descent (2019) — 119 citations
• ZerO Initialization: Initializing Residual Networks with only Zeros and Ones (2021) — 12 citations