Wojciech Jaśkowski

Reinforcement Learning · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 80 researchers and is trusted by 185 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 66 citations into it; #766 of 2,242 by trust here, #523 by citations
• Artificial Intelligence — 60 citations into it; #5,977 of 35,345 by trust here, #4,923 by citations
• Computational Theory and Mathematics — 14 citations into it
• Control and Systems Engineering — 10 citations into it
• Model Architectures — 10 citations into it
• Management Science and Operations Research — 9 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it; #2,769 of 21,575 by trust here, #4,402 by citations
• Optimization & Training — 7 citations into it

Whom Wojciech Jaśkowski trusts overall

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

1. J. Urgen Schmidhuber
2. Geoffrey E. Hinton
3. Richard S. Sutton
4. Ronald J. Williams
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Yoshua Bengio
8. Sepp Hochreiter
9. Sergey Levine
10. Andrew Barto

Who trusts Wojciech Jaśkowski overall

1. J. Urgen Schmidhuber
2. Pawel Ladosz
3. Brandon Amos
4. Emilio Parisotto
5. Sergey Levine
6. Kavosh Asadi
7. Devendra Singh Chaplot
8. Ruslan Salakhutdinov
9. R. Sekar
10. Benjamin Eysenbach

Papers from Wojciech Jaśkowski's perspective

Ordered by how strongly the researchers Wojciech Jaśkowski 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• ViZDoom: A Doom-based AI research platform for visual reinforcement learning (2016) — 752 citations
• Model-Based Active Exploration (2018) — 199 citations
• Training Agents using Upside-Down Reinforcement Learning (2019) — 140 citations