Louis Kirsch

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 102 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it; #12,159 of 35,345 by trust here, #12,387 by citations
• Reinforcement Learning — 34 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Optimization & Training — 21 citations into it
• Model Architectures — 17 citations into it; #1,047 of 2,278 by trust here, #1,726 by citations
• Meta & Continual Learning — 11 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Natural Language Processing — 8 citations into it

Whom Louis Kirsch trusts overall

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

1. J. Urgen Schmidhuber
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ronald J. Williams
5. Yoshua Bengio
6. Diederik P. Kingma
7. John Schulman
8. Richard S. Sutton
9. Alex Graves
10. Chelsea Finn

Who trusts Louis Kirsch overall

1. Jorge Armando Mendez Mendez
2. Sae-Young Chung
3. Eric Eaton
4. Clemens Rosenbaum
5. Kanika Madan
6. Ignacio Cases
7. Anirudh Goyal
8. Seungwon Lee
9. Oleksiy Ostapenko
10. Junhyuk Oh

Papers from Louis Kirsch's perspective

Ordered by how strongly the researchers Louis Kirsch trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Modular Networks: Learning to Decompose Neural Computation (2018) — 127 citations
• Improving Generalization in Meta Reinforcement Learning using Learned Objectives (2019) — 132 citations
• Transfer Learning for Speech Recognition on a Budget (2017) — 147 citations
• Introducing Symmetries to Black Box Meta Reinforcement Learning (2021) — 36 citations