Alexander Novikov

Artificial Intelligence · with Freitas · 2015–2022

A researcher in the Science corpus: 14 papers; trusts 315 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 454 citations into it; #7,781 of 35,345 by trust here, #2,929 by citations
• Reinforcement Learning — 420 citations into it; #1,205 of 2,242 by trust here, #380 by citations
• Computer Vision and Pattern Recognition — 154 citations into it; #18,823 of 21,575 by trust here, #4,658 by citations
• Control and Systems Engineering — 98 citations into it
• Management Science and Operations Research — 83 citations into it; #11,858 of 11,946 by trust here, #3,825 by citations
• Computational Theory and Mathematics — 74 citations into it
• Evaluation & Benchmarks — 57 citations into it
• Optimization & Training — 54 citations into it; #2,591 of 2,601 by trust here, #1,280 by citations

Whom Alexander Novikov trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Diederik P. Kingma
5. Richard S. Sutton
6. Yoshua Bengio
7. Jimmy Ba
8. Ilya Sutskever
9. Karen Simonyan
10. Kaiming He

Who trusts Alexander Novikov overall

1. Yerlan Idelbayev
2. M. A. Carreira-Perpiñán
3. Rose Yu
4. Jean Kossaifi
5. Yongxin Yang
6. Vadim Lebedev
7. Hiroki Kanagawa
8. Gavia Gray
9. By Lei Deng
10. Pavel Izmailov

Papers from Alexander Novikov's perspective

Ordered by how strongly the researchers Alexander Novikov 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Generalist Agent (2022) — 1,117 citations
• Scaling data-driven robotics with reward sketching and batch reinforcement learning (2019) — 160 citations
• Acme: A Research Framework for Distributed Reinforcement Learning (2020) — 248 citations
• Critic Regularized Regression (2020) — 389 citations
• RL Unplugged: Benchmarks for Offline Reinforcement Learning (2020) — 74 citations
• Tensorizing Neural Networks (2015) — 998 citations
• Hyperparameter Selection for Offline Reinforcement Learning (2020) — 163 citations
• Benchmarks for Deep Off-Policy Evaluation (2021) — 117 citations
• Ultimate tensorization: compressing convolutional and FC layers alike (2016) — 206 citations
• A Framework for Data-Driven Robotics (2019) — 30 citations
• Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition (2017) — 69 citations
• Discovering faster matrix multiplication algorithms with reinforcement learning (2022) — 714 citations
• RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning (2020)
• Task-Relevant Adversarial Imitation Learning (2019) — 65 citations