Alex Tamkin
Artificial Intelligence · 2019–2024
A researcher in the Science corpus: 7 papers; trusts 43 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 112 citations into it; #33,311 of 35,345 by trust here, #2,322 by citations
- Natural Language Processing — 46 citations into it
- Safety, Robustness & Interpretability — 38 citations into it
- Computer Vision and Pattern Recognition — 18 citations into it
- Representation & Self-Supervised Learning — 12 citations into it; #2,961 of 3,145 by trust here, #2,863 by citations
- Model Architectures — 9 citations into it
- Management Science and Operations Research — 6 citations into it
- Optimization & Training — 5 citations into it
Whom Alex Tamkin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Kaiming He
- Geoffrey E. Hinton
- Jimmy Ba
- Richard Socher
- Alex Krizhevsky
- Ilya Sutskever
- Yann LeCun
- Maithra Raghu
Who trusts Alex Tamkin overall
Papers from Alex Tamkin's perspective
Ordered by how strongly the researchers Alex Tamkin trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models (2021) — 333 citations
- Investigating Transferability in Pretrained Language Models (2020) — 49 citations
- Recursive Routing Networks: Learning to Compose Modules for Language Understanding (2019) — 29 citations
- Many-shot Jailbreaking (2024) — 300 citations
- Studying Large Language Model Generalization with Influence Functions (2023) — 343 citations
- Towards Measuring the Representation of Subjective Global Opinions in Language Models (2023) — 451 citations