Hippolyt Ritter

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 96 researchers and is trusted by 98 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #2,833 of 35,345 by trust here, #5,495 by citations
• Optimization & Training — 35 citations into it; #1,026 of 2,601 by trust here, #1,585 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #3,507 of 21,575 by trust here, #4,690 by citations
• Probabilistic & Bayesian Methods — 19 citations into it; #640 of 1,525 by trust here, #865 by citations
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it; #458 of 3,145 by trust here, #866 by citations
• Statistical and Nonlinear Physics — 7 citations into it; #2,235 of 10,127 by trust here, #4,281 by citations
• Management Science and Operations Research — 6 citations into it

Whom Hippolyt Ritter trusts overall

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

1. James Martens
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yann LeCun
5. David John Cameron MacKay
6. Radford M. Neal
7. Yoshua Bengio
8. Diederik P. Kingma
9. Ilya Sutskever
10. Max Welling

Who trusts Hippolyt Ritter overall

1. Tyler L. Hayes
2. Jorge Armando Mendez Mendez
3. Wesley J. Maddox
4. Janghyeon Lee
5. Timothée Lesort
6. Gyuhak Kim
7. Zheda Mai
8. Mehrdad Farajtabar
9. Pavel Izmailov
10. Frederik Benzing

Papers from Hippolyt Ritter's perspective

Ordered by how strongly the researchers Hippolyt Ritter 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting (2018) — 384 citations
• Practical Gauss-Newton Optimisation for Deep Learning (2017) — 266 citations
• A Scalable Laplace Approximation for Neural Networks (2018) — 513 citations