Max Schwarzer

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 5 papers; trusts 248 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 150 citations into it; #25,101 of 35,345 by trust here, #16,771 by citations
• Reinforcement Learning — 91 citations into it
• Computer Vision and Pattern Recognition — 53 citations into it
• Representation & Self-Supervised Learning — 30 citations into it
• Management Science and Operations Research — 29 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Optimization & Training — 17 citations into it
• Natural Language Processing — 13 citations into it

Whom Max Schwarzer trusts overall

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

1. Geoffrey E. Hinton
2. Richard S. Sutton
3. Yoshua Bengio
4. Kaiming He
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Yann LeCun
8. Ilya Sutskever
9. Marc G. Bellemare
10. Sergey Ioffe

Who trusts Max Schwarzer overall

1. Vincent Micheli
2. Evgenii Nikishin
3. Yilun Du
4. Konstantin Schürholt
5. Rishabh Agarwal
6. Eloi Alonso
7. Anthony Hu
8. Weirui Ye
9. Franccois Fleuret
10. Chuang Gan

Papers from Max Schwarzer's perspective

Ordered by how strongly the researchers Max Schwarzer 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Reinforcement Learning at the Edge of the Statistical Precipice (2021) — 950 citations
• Data-Efficient Reinforcement Learning with Self-Predictive Representations (2020) — 426 citations
• The Primacy Bias in Deep Reinforcement Learning (2022) — 307 citations
• Iterated learning for emergent systematicity in VQA (2021) — 28 citations
• GAIT: A Geometric Approach to Information Theory (2019) — 12 citations