Daniel M. Ziegler

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 9 papers; trusts 92 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 320 citations into it; #5,440 of 35,345 by trust here, #156 by citations
• Natural Language Processing — 229 citations into it; #3,321 of 5,688 by trust here, #904 by citations
• Computer Vision and Pattern Recognition — 115 citations into it
• Reinforcement Learning — 57 citations into it
• Large Language Models — 53 citations into it; #255 of 799 by trust here, #39 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Alignment & RLHF — 19 citations into it; #16 of 62 by trust here, #13 by citations
• Optimization & Training — 18 citations into it

Whom Daniel M. Ziegler trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alec Radford
4. Yoshua Bengio
5. Ilya Sutskever
6. Alex Krizhevsky
7. Yann LeCun
8. Kaiming He
9. Jimmy Ba
10. Alex Graves

Who trusts Daniel M. Ziegler overall

1. J'er'emy Scheurer
2. Tomasz Korbak
3. Angelica Chen
4. Hanze Dong
5. Emilio Ferrara
6. Ethan Perez
7. Jon Ander Campos
8. Yu Meng
9. Xiangyu Peng
10. Wei Xiong

Papers from Daniel M. Ziegler's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Language Models are Few-Shot Learners (2020) — 61,005 citations
• Fine-Tuning Language Models from Human Preferences (2019) — 2,638 citations
• Scaling Laws for Autoregressive Generative Modeling (2020) — 639 citations
• Recursively Summarizing Books with Human Feedback (2021) — 371 citations
• Adversarial Training for High-Stakes Reliability (2022) — 79 citations
• Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training (2024) — 489 citations
• Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT (2021)
• Learning to summarize from human feedback (2020) — 3,335 citations
• Learning to summarize with human feedback (2020) — 121 citations