Vitchyr H. Pong

Artificial Intelligence · 2017–2025

A researcher in the Science corpus: 8 papers; trusts 189 researchers and is trusted by 79 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 156 citations into it; #3,062 of 35,345 by trust here, #4,316 by citations
• Reinforcement Learning — 156 citations into it; #478 of 2,242 by trust here, #478 by citations
• Control and Systems Engineering — 64 citations into it; #1,967 of 8,257 by trust here, #618 by citations
• Computer Vision and Pattern Recognition — 59 citations into it
• Computational Theory and Mathematics — 31 citations into it; #4,183 of 12,182 by trust here, #2,629 by citations
• Robotics & Control — 22 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Generative Models — 18 citations into it

Whom Vitchyr H. Pong trusts overall

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

1. Diederik P. Kingma
2. Sergey Levine
3. Richard S. Sutton
4. E. Todorov
5. Geoffrey E. Hinton
6. Pieter Abbeel
7. Yoshua Bengio
8. Alex Krizhevsky
9. Timothy P. Lillicrap
10. J. Urgen Schmidhuber

Who trusts Vitchyr H. Pong overall

1. Sergey Levine
2. Benjamin Eysenbach
3. Abhishek Gupta
4. Corey Lynch
5. Chelsea Finn
6. Dibya Ghosh
7. Ruslan Salakhutdinov
8. Suraj Nair
9. Pieter Abbeel
10. Soroush Nasiriany

Papers from Vitchyr H. Pong's perspective

Ordered by how strongly the researchers Vitchyr H. Pong 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Visual Reinforcement Learning with Imagined Goals (2018) — 603 citations
• Temporal Difference Models: Model-Free Deep RL for Model-Based Control (2018) — 266 citations
• Skew-Fit: State-Covering Self-Supervised Reinforcement Learning (2019) — 313 citations
• Composable Deep Reinforcement Learning for Robotic Manipulation (2018) — 254 citations
• Uncertainty-Aware Reinforcement Learning for Collision Avoidance (2017) — 337 citations
• Planning with Goal-Conditioned Policies (2019) — 255 citations
• OpenAI GPT-5 System Card (2025) — 667 citations
• GPT-4 Technical Report (2023) — 26,133 citations