Konrad Zolna

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 13 papers; trusts 245 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 417 citations into it; #23,391 of 35,345 by trust here, #4,254 by citations
• Reinforcement Learning — 351 citations into it; #1,630 of 2,242 by trust here, #436 by citations
• Computer Vision and Pattern Recognition — 169 citations into it
• Control and Systems Engineering — 91 citations into it
• Management Science and Operations Research — 57 citations into it
• Natural Language Processing — 55 citations into it
• Model Architectures — 47 citations into it
• Optimization & Training — 47 citations into it; #2,590 of 2,601 by trust here, #1,178 by citations

Whom Konrad Zolna trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Richard S. Sutton
5. Alex Krizhevsky
6. Yann LeCun
7. Kaiming He
8. J. Urgen Schmidhuber
9. Sepp Hochreiter
10. Ilya Sutskever

Who trusts Konrad Zolna overall

1. Danfei Xu
2. Sho Takase
3. T. Paine
4. Ateret Anaby-Tavor
5. Xiaobo Liang
6. Misha Denil
7. David Brandfonbrener
8. Rafael Figueiredo Prudencio
9. Jun Suzuki
10. Ilya Kostrikov

Papers from Konrad Zolna's perspective

Ordered by how strongly the researchers Konrad Zolna 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Generalist Agent (2022) — 1,117 citations
• Scaling data-driven robotics with reward sketching and batch reinforcement learning (2019) — 160 citations
• Critic Regularized Regression (2020) — 389 citations
• RL Unplugged: Benchmarks for Offline Reinforcement Learning (2020) — 74 citations
• Hyperparameter Selection for Offline Reinforcement Learning (2020) — 163 citations
• Fraternal Dropout (2017) — 54 citations
• A Framework for Data-Driven Robotics (2019) — 30 citations
• Regularized Behavior Value Estimation (2021) — 43 citations
• Focused Hierarchical RNNs for Conditional Sequence Processing (2018) — 25 citations
• Genie: Generative Interactive Environments (2024) — 696 citations
• RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning (2020)
• Task-Relevant Adversarial Imitation Learning (2019) — 65 citations
• User Modeling Using LSTM Networks (2017) — 10 citations