T. Paine

Artificial Intelligence · arXiv.org · 2013–2025

A researcher in the Science corpus: 21 papers; trusts 568 researchers and is trusted by 101 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 492 citations into it; #11,070 of 35,345 by trust here, #1,616 by citations
• Reinforcement Learning — 440 citations into it; #1,398 of 2,242 by trust here, #260 by citations
• Computer Vision and Pattern Recognition — 187 citations into it; #3,409 of 21,575 by trust here, #3,089 by citations
• Management Science and Operations Research — 105 citations into it; #11,623 of 11,946 by trust here, #3,247 by citations
• Control and Systems Engineering — 82 citations into it
• Computational Theory and Mathematics — 65 citations into it
• Evaluation & Benchmarks — 62 citations into it
• Optimization & Training — 61 citations into it; #1,976 of 2,601 by trust here, #1,448 by citations

Whom T. Paine trusts overall

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

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

Who trusts T. Paine overall

1. Xiangru Lian
2. Alex Krizhevsky
3. Sergey Levine
4. Ilya Kostrikov
5. Waseem Rawat
6. Ofir Nachum
7. Scott Fujimoto
8. Prajit Ramachandran
9. Xin Eric Wang
10. Quoc V. Le

Papers from T. Paine's perspective

Ordered by how strongly the researchers T. Paine 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

• Grandmaster level in StarCraft II using multi-agent reinforcement learning (2019) — 4,437 citations
• Playing hard exploration games by watching YouTube (2018) — 286 citations
• Fast Generation for Convolutional Autoregressive Models (2017) — 67 citations
• Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions (2017) — 90 citations
• Acme: A Research Framework for Distributed Reinforcement Learning (2020) — 248 citations
• Seq-NMS for Video Object Detection (2016) — 323 citations
• RL Unplugged: Benchmarks for Offline Reinforcement Learning (2020) — 74 citations
• Hyperparameter Selection for Offline Reinforcement Learning (2020) — 163 citations
• Large-Scale Visual Speech Recognition (2018) — 172 citations
• GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training (2013) — 100 citations
• Benchmarks for Deep Off-Policy Evaluation (2021) — 117 citations
• One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL (2018) — 26 citations
• Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization (2021) — 53 citations
• Fast Wavenet Generation Algorithm (2016) — 88 citations
• An Analysis of Unsupervised Pre-training in Light of Recent Advances (2014) — 56 citations
• Regularized Behavior Value Estimation (2021) — 43 citations
• Reinforced Self-Training (ReST) for Language Modeling (2023) — 473 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning (2020)