Stewart Slocum

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 3 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it
• Molecular Biology — 39 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Optimization & Training — 5 citations into it

Whom Stewart Slocum trusts overall

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

1. Diederik P. Kingma
2. Frances H. Arnold
3. Reuven Y. Rubinstein

Papers from Stewart Slocum's perspective

Ordered by how strongly the researchers Stewart Slocum trusts endorse each paper. Same corpus, a different order for every researcher.

1. GENERATIVE ADVERSARIAL NETS
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
4. Human-level control through deep reinforcement learning
5. Proximal Policy Optimization Algorithms (arXiv.org)
6. Wasserstein GAN
7. Scikit-learn: Machine Learning in Python
8. Attention is All you Need (Neural Information Processing Systems)
9. Maximum Entropy Inverse Reinforcement Learning
10. Training language models to follow instructions with human feedback (Neural Information Processing Systems)

Their papers

• Designing Peptides on a Quantum Computer (2019) — 58 citations
• AdaLead: A simple and robust adaptive greedy search algorithm for sequence design (2020) — 113 citations
• Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (2023) — 934 citations