Mel Vecerík

Reinforcement Learning · 2018–2023

A researcher in the Science corpus: 6 papers; trusts 146 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 144 citations into it
• Artificial Intelligence — 138 citations into it; #23,711 of 35,345 by trust here, #8,283 by citations
• Computer Vision and Pattern Recognition — 123 citations into it
• Control and Systems Engineering — 62 citations into it; #6,078 of 8,257 by trust here, #1,165 by citations
• Aerospace Engineering — 22 citations into it
• Generative Models — 18 citations into it
• Management Science and Operations Research — 16 citations into it
• Optimization & Training — 10 citations into it

Whom Mel Vecerík trusts overall

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

1. Diederik P. Kingma
2. David G. Lowe
3. Geoffrey E. Hinton
4. Sergey Levine
5. Richard S. Sutton
6. Chelsea Finn
7. Yoshua Bengio
8. Alex Krizhevsky
9. Yann LeCun
10. Stéphane Ross

Who trusts Mel Vecerík overall

1. Danfei Xu
2. Serkan Cabi
3. Gabriel Dulac-Arnold
4. Cosmin Paduraru
5. Avi Singh
6. Kevin Zakka
7. Misha Denil
8. Daniel Jaymin Mankowitz
9. Jonathan D. Chang
10. Yiming Ding

Papers from Mel Vecerík's perspective

Ordered by how strongly the researchers Mel Vecerík 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Observe and Look Further: Achieving Consistent Performance on Atari (2018) — 127 citations
• Scaling data-driven robotics with reward sketching and batch reinforcement learning (2019) — 160 citations
• A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning (2018) — 118 citations
• TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement (2023) — 336 citations
• A Framework for Data-Driven Robotics (2019) — 30 citations
• Generative predecessor models for sample-efficient imitation learning (2019) — 32 citations