Yannick Schroecker

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 5 papers; trusts 143 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 153 citations into it; #23,027 of 35,345 by trust here, #16,771 by citations
• Reinforcement Learning — 107 citations into it
• Computer Vision and Pattern Recognition — 70 citations into it; #14,298 of 21,575 by trust here, #12,992 by citations
• Control and Systems Engineering — 28 citations into it
• Optimization & Training — 28 citations into it
• Management Science and Operations Research — 17 citations into it
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Yannick Schroecker trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Stéphane Ross
5. Yoshua Bengio
6. J. Urgen Schmidhuber
7. Individualized Treat
8. Alex Krizhevsky
9. Chelsea Finn
10. Andrew Y. Ng

Who trusts Yannick Schroecker overall

1. Yiming Ding
2. Faraz Torabi
3. Mel Vecerík
4. Yuping Luo
5. Carlos Florensa
6. Jonathan Scholz
7. Robert Dadashi

Papers from Yannick Schroecker's perspective

Ordered by how strongly the researchers Yannick Schroecker 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Imitating Latent Policies from Observation (2018) — 179 citations
• Generative predecessor models for sample-efficient imitation learning (2019) — 32 citations
• Bootstrapped Meta-Learning (2021) — 62 citations
• Human-Timescale Adaptation in an Open-Ended Task Space (2023) — 163 citations
• State Aware Imitation Learning (2017) — 28 citations