Sebastian Flennerhag

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 5 papers; trusts 250 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 131 citations into it
• Computer Vision and Pattern Recognition — 69 citations into it
• Optimization & Training — 62 citations into it
• Reinforcement Learning — 43 citations into it
• Representation & Self-Supervised Learning — 37 citations into it
• Meta & Continual Learning — 29 citations into it
• Model Architectures — 22 citations into it
• Computational Theory and Mathematics — 15 citations into it

Whom Sebastian Flennerhag trusts overall

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

1. J. Urgen Schmidhuber
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Richard S. Sutton
6. Yann LeCun
7. Alex Krizhevsky
8. Kaiming He
9. Oriol Vinyals
10. Ilya Sutskever

Who trusts Sebastian Flennerhag overall

1. Lin Wang
2. Christian Simon
3. Johannes von Oswald
4. Sébastien M. R. Arnold
5. Jianping Gou
6. Trapit Bansal
7. Junhyuk Oh
8. Eric Mitchell
9. K. J. Joseph
10. Louis Kirsch

Papers from Sebastian Flennerhag's perspective

Ordered by how strongly the researchers Sebastian Flennerhag 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Meta-Learning with Warped Gradient Descent (2019) — 221 citations
• Bootstrapped Meta-Learning (2021) — 62 citations
• Introducing Symmetries to Black Box Meta Reinforcement Learning (2021) — 36 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Transferring Knowledge across Learning Processes (2018) — 65 citations