Viktor Makoviychuk

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 26 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #34,954 of 35,345 by trust here, #10,430 by citations
• Reinforcement Learning — 72 citations into it
• Control and Systems Engineering — 38 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it; #21,410 of 21,575 by trust here, #12,419 by citations
• Robotics & Control — 25 citations into it; #595 of 609 by trust here, #262 by citations
• Mechanical Engineering — 15 citations into it
• Optimization & Training — 8 citations into it

Whom Viktor Makoviychuk trusts overall

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

1. John Schulman
2. E. Todorov
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Marcin Andrychowicz
6. Sergey Levine
7. Richard S. Sutton
8. Marc Peter Deisenroth
9. Alex Krizhevsky
10. Yoshua Bengio

Who trusts Viktor Makoviychuk overall

1. Gabriel B. Margolis
2. N. Rudin
3. Ilija Radosavovic
4. David Hoeller
5. Tete Xiao
6. Tao Chen
7. Xuanlin Li
8. Ye Yuan
9. William S. Peebles
10. Philipp Wu

Papers from Viktor Makoviychuk's perspective

Ordered by how strongly the researchers Viktor Makoviychuk 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. Et al
9. Asynchronous Methods for Deep Reinforcement Learning
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning (2021) — 1,851 citations
• Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience (2018) — 623 citations
• GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning (2018) — 245 citations
• Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge? (2020) — 584 citations