Jeremias Knoblauch

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 12 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #27,958 of 35,345 by trust here, #17,110 by citations
• Statistics and Probability — 28 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it; #17,161 of 21,575 by trust here, #11,498 by citations
• Probabilistic & Bayesian Methods — 13 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Statistical Learning Theory — 5 citations into it; #923 of 1,038 by trust here, #577 by citations

Whom Jeremias Knoblauch trusts overall

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

1. Manfred Opper
2. Hans Raj Tiwary
3. Thorsten Theobald
4. Aapo Hyvärinen
5. Geoffrey E. Hinton
6. Hippolyt Ritter
7. Sylvestre-Alvise Rebuffi
8. Michalis K. Titsias
9. Andrei A. Rusu
10. Razvan Pascanu

Who trusts Jeremias Knoblauch overall

1. Xikun Zhang
2. Sungmin Cha
3. Jonas Frey
4. Zheda Mai
5. Timothée Lesort
6. Maciej Wołczyk
7. Krishnan Raghavan
8. Fei Ye
9. Jonathan Brophy
10. Liangzu Peng

Papers from Jeremias Knoblauch's perspective

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

• Optimal Continual Learning has Perfect Memory and is NP-hard (2020) — 117 citations
• Robust generalised Bayesian inference for intractable likelihoods (2021) — 109 citations