Oleh Rybkin

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 386 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 152 citations into it
• Computer Vision and Pattern Recognition — 119 citations into it
• Reinforcement Learning — 89 citations into it
• Generative Models — 74 citations into it
• Control and Systems Engineering — 38 citations into it
• Representation & Self-Supervised Learning — 32 citations into it
• Probabilistic & Bayesian Methods — 21 citations into it
• Model Architectures — 18 citations into it

Whom Oleh Rybkin trusts overall

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

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

Who trusts Oleh Rybkin overall

1. Danijar Hafner
2. Masashi Okada
3. Lukasz Kaiser
4. Nicklas Hansen

Papers from Oleh Rybkin's perspective

Ordered by how strongly the researchers Oleh Rybkin 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. 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

• Planning to Explore via Self-Supervised World Models (2020) — 514 citations
• Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning (2019) — 32 citations
• Simple and Effective VAE Training with Calibrated Decoders (2020) — 126 citations
• KeyIn: Discovering Subgoal Structure with Keyframe-based Video Prediction (2019) — 7 citations
• Unsupervised Learning of Sensorimotor Affordances by Stochastic Future Prediction (2018) — 6 citations