Jonas Schneider

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 9 papers; trusts 202 researchers and is trusted by 269 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 239 citations into it; #3,066 of 35,345 by trust here, #1,081 by citations
• Reinforcement Learning — 205 citations into it; #152 of 2,242 by trust here, #52 by citations
• Control and Systems Engineering — 123 citations into it; #3,341 of 8,257 by trust here, #81 by citations
• Computer Vision and Pattern Recognition — 118 citations into it; #3,466 of 21,575 by trust here, #2,872 by citations
• Robotics & Control — 43 citations into it; #387 of 609 by trust here, #27 by citations
• Representation & Self-Supervised Learning — 40 citations into it
• Optimization & Training — 37 citations into it
• Computational Theory and Mathematics — 32 citations into it

Whom Jonas Schneider trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. J. Urgen Schmidhuber
4. Yoshua Bengio
5. Alex Krizhevsky
6. Ilya Sutskever
7. Richard S. Sutton
8. E. Todorov
9. Jimmy Ba
10. Sergey Levine

Who trusts Jonas Schneider overall

1. Sergey Levine
2. Pieter Abbeel
3. Chelsea Finn
4. Marcin Andrychowicz
5. J. Urgen Schmidhuber
6. Stephen James
7. David R Ha
8. Yoshua Bengio
9. Ofir Nachum
10. Benjamin Eysenbach

Papers from Jonas Schneider's perspective

Ordered by how strongly the researchers Jonas Schneider 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Domain randomization for transferring deep neural networks from simulation to the real world (2017) — 3,883 citations
• Learning dexterous in-hand manipulation (2018) — 2,231 citations
• One-Shot Imitation Learning (2017) — 745 citations
• Dota 2 with Large Scale Deep Reinforcement Learning (2019) — 2,179 citations
• Solving Rubik's Cube with a Robot Hand (2019) — 1,497 citations
• Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research (2018) — 642 citations
• Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model (2016) — 270 citations
• Hindsight Experience Replay (2017) — 2,808 citations
• OpenAI Gym (2016) — 5,596 citations