Kamal Ndousse

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 13 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 392 citations into it
• Reinforcement Learning — 44 citations into it

Whom Kamal Ndousse trusts overall

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

1. John Schulman
2. Karl Cobbe
3. Diederik P. Kingma
4. Oleg Klimov
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Andrew Y. Ng
8. Filip Wolski
9. Alex Krizhevsky
10. Sepp Hochreiter

Papers from Kamal Ndousse's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Proximal Policy Optimization Algorithms (arXiv.org)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback (2022) — 4,254 citations
• Constitutional AI: Harmlessness from AI Feedback (2022) — 3,377 citations
• Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned (2022) — 837 citations
• Predictability and Surprise in Large Generative Models (2022) — 365 citations
• In-context Learning and Induction Heads (2022) — 942 citations
• Language Models (Mostly) Know What They Know (2022) — 1,782 citations
• Discovering Language Model Behaviors with Model-Written Evaluations (2022) — 917 citations
• The Capacity for Moral Self-Correction in Large Language Models (2023) — 214 citations
• Measuring Progress on Scalable Oversight for Large Language Models (2022) — 226 citations
• Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training (2024) — 489 citations
• Towards Understanding Sycophancy in Language Models (2023) — 1,082 citations
• A General Language Assistant as a Laboratory for Alignment (2021) — 1,175 citations
• Emergent Social Learning via Multi-agent Reinforcement Learning (2020) — 69 citations