Aldo Pacchiano

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 8 papers; trusts 156 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 170 citations into it; #30,742 of 35,345 by trust here, #23,731 by citations
• Reinforcement Learning — 141 citations into it; #2,025 of 2,242 by trust here, #1,932 by citations
• Management Science and Operations Research — 57 citations into it
• Optimization & Training — 30 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Safety Research — 22 citations into it
• Safety, Robustness & Interpretability — 22 citations into it; #2,127 of 2,251 by trust here, #2,014 by citations
• Computational Theory and Mathematics — 20 citations into it

Whom Aldo Pacchiano trusts overall

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

1. Aadirupa Saha
2. Richard S. Sutton
3. John Schulman
4. Ronald J. Williams
5. Diederik P. Kingma
6. Geoffrey E. Hinton
7. J. Urgen Schmidhuber
8. Christian Wirth
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Aldo Pacchiano overall

1. Wei Xiong
2. Jack Parker-Holder
3. Silvia Chiappa
4. Krzysztof Marcin Choromanski
5. Luca Oneto
6. Banghua Zhu
7. Aadirupa Saha
8. Hanze Dong

Papers from Aldo Pacchiano's perspective

Ordered by how strongly the researchers Aldo Pacchiano 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Dueling RL: Reinforcement Learning with Trajectory Preferences (2021) — 120 citations
• Ready Policy One: World Building Through Active Learning (2020) — 54 citations
• On the Theory of Reinforcement Learning with Once-per-Episode Feedback (2021) — 37 citations
• Learning to Score Behaviors for Guided Policy Optimization (2019) — 46 citations
• Effective Diversity in Population-Based Reinforcement Learning (2020) — 191 citations
• A General Approach to Fairness with Optimal Transport (2020) — 97 citations
• KAMA-NNs: Low-dimensional Rotation Based Neural Networks (2019) — 6 citations
• Wasserstein Fair Classification (2019) — 216 citations