Stefan Zohren

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 450 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 209 citations into it; #29,831 of 35,345 by trust here, #25,040 by citations
• Computer Vision and Pattern Recognition — 88 citations into it
• Management Science and Operations Research — 69 citations into it; #7,099 of 11,946 by trust here, #5,316 by citations
• Time-Series & Forecasting — 46 citations into it; #277 of 381 by trust here, #173 by citations
• Model Architectures — 43 citations into it
• Representation & Self-Supervised Learning — 43 citations into it
• Signal Processing — 41 citations into it; #6,462 of 7,572 by trust here, #4,560 by citations
• Probabilistic & Bayesian Methods — 37 citations into it; #1,387 of 1,525 by trust here, #1,487 by citations

Whom Stefan Zohren trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Sepp Hochreiter
6. Ilya Sutskever
7. Yann LeCun
8. Radford M. Neal
9. Alex Graves
10. J. Urgen Schmidhuber

Who trusts Stefan Zohren overall

1. Bryan Lim
2. Weiwei Jiang
3. Si Chen
4. Guillermo Alonso Iglesias

Papers from Stefan Zohren's perspective

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

Their papers

• Same State, Different Task: Continual Reinforcement Learning without Interference (2021) — 62 citations
• Hierarchical Indian buffet neural networks for Bayesian continual learning (2019) — 26 citations
• Time-series forecasting with deep learning: a survey (2020) — 2,001 citations
• Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction (2019) — 51 citations
• Indian Buffet Neural Networks for Continual Learning (2019) — 6 citations
• Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019) — 103 citations
• DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018) — 285 citations