Michal Rolinek

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 208 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #15,547 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #10,064 of 21,575 by trust here, #14,151 by citations
• Optimization & Training — 20 citations into it; #1,437 of 2,601 by trust here, #2,215 by citations
• Generative Models — 19 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Model Architectures — 11 citations into it
• Reinforcement Learning — 8 citations into it
• Computational Theory and Mathematics — 7 citations into it

Whom Michal Rolinek trusts overall

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

1. Diederik P. Kingma
2. Yann LeCun
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Alex Graves
7. Kaiming He
8. L. Eon Bottou
9. Xavier Glorot
10. Ilya Sutskever

Who trusts Michal Rolinek overall

1. Nicolas Loizou
2. Sharan Vaswani
3. Robert Mansel Gower
4. Issam Hadj Laradji
5. Sunny Duan
6. Othmane Sebbouh
7. Simon Lacoste-Julien
8. Aaron Mishkin
9. Nicholas Watters
10. Kien Do

Papers from Michal Rolinek's perspective

Ordered by how strongly the researchers Michal Rolinek 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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

• Variational Autoencoders Pursue PCA Directions (by Accident) (2018) — 176 citations
• Differentiation of Blackbox Combinatorial Solvers (2019) — 350 citations
• L4: Practical loss-based stepsize adaptation for deep learning (2018) — 68 citations