Cem Anil
Artificial Intelligence · 2018–2024
A researcher in the Science corpus: 7 papers; trusts 186 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 122 citations into it; #17,303 of 35,345 by trust here, #4,682 by citations
- Computer Vision and Pattern Recognition — 65 citations into it; #10,971 of 21,575 by trust here, #8,831 by citations
- Safety, Robustness & Interpretability — 26 citations into it
- Optimization & Training — 19 citations into it
- Model Architectures — 18 citations into it
- Computational Theory and Mathematics — 16 citations into it
- Scaling & Learning Theory — 11 citations into it
- Management Science and Operations Research — 7 citations into it
Whom Cem Anil trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Yann LeCun
- Jimmy Ba
- Ilya Sutskever
- Christian Szegedy
- Ian J. Goodfellow
Who trusts Cem Anil overall
- Klas Leino
- Yan Zhang
- Jeremy M. Cohen
- Yujia Huang
- Mahyar Fazlyab
- Hsueh-Ti Derek Liu
- Samy Wu Fung
- Jeremiah Zhe Liu
- Tianlong Chen
- Howard Heaton
Papers from Cem Anil's perspective
Ordered by how strongly the researchers Cem Anil 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Solving Quantitative Reasoning Problems with Language Models (2022) — 1,845 citations
- Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization (2018) — 1,009 citations
- Sorting out Lipschitz function approximation (2018) — 386 citations
- Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training (2024) — 489 citations
- Many-shot Jailbreaking (2024) — 300 citations
- Studying Large Language Model Generalization with Influence Functions (2023) — 343 citations
- Learning to Give Checkable Answers with Prover-Verifier Games (2021) — 26 citations