Samuel Marks

Artificial Intelligence · 2023–2024

A researcher in the Science corpus: 3 papers; trusts 24 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 151 citations into it
• Safety, Robustness & Interpretability — 44 citations into it
• Natural Language Processing — 35 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Health Informatics — 9 citations into it
• Management Science and Operations Research — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Causal Inference & Discovery — 6 citations into it

Whom Samuel Marks trusts overall

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

1. Kenneth Li
2. Yoshua Bengio
3. Guillaume Alain
4. Amos Azaria
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Collin Burns
8. Alex Krizhevsky
9. Judea Pearl
10. Ilya Sutskever

Who trusts Samuel Marks overall

1. Chris Wendler
2. Andy Arditi
3. Veniamin Veselovsky
4. Neel Nanda
5. Giovanni Monea

Papers from Samuel Marks's perspective

Ordered by how strongly the researchers Samuel Marks 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. Attention is All you Need (Neural Information Processing Systems)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models (2024) — 394 citations
• Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (2023) — 934 citations
• The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets (2023) — 622 citations