Danny Hernandez
Artificial Intelligence · 2020–2023
A researcher in the Science corpus: 16 papers; trusts 200 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 468 citations into it; #24,649 of 35,345 by trust here, #1,289 by citations
- Safety, Robustness & Interpretability — 183 citations into it
- Natural Language Processing — 154 citations into it
- Large Language Models — 135 citations into it
- Computer Vision and Pattern Recognition — 116 citations into it; #15,213 of 21,575 by trust here, #2,718 by citations
- Model Architectures — 21 citations into it
- Efficiency & Compression — 17 citations into it; #864 of 1,264 by trust here, #1,160 by citations
- Optimization & Training — 7 citations into it
Whom Danny Hernandez trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ilya Sutskever
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Quoc V. Le
- Sergey Ioffe
- Christian Szegedy
- Yann LeCun
Who trusts Danny Hernandez overall
- Chaoyang He
- Stella Biderman
- Anders Andreassen
- Neil C. Thompson
- Gabriele Prato
- Carole-Jean Wu
- Murali Annavaram
- Yi Tay
- Mengzhou Xia
- Udit Gupta
Papers from Danny Hernandez's perspective
Ordered by how strongly the researchers Danny Hernandez trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
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
- Scaling Laws for Transfer (2021) — 330 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
- Towards Measuring the Representation of Subjective Global Opinions in Language Models (2023) — 451 citations
- Measuring Faithfulness in Chain-of-Thought Reasoning (2023) — 499 citations
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2023) — 266 citations
- Scaling Laws and Interpretability of Learning from Repeated Data (2022) — 166 citations
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
- A General Language Assistant as a Laboratory for Alignment (2021) — 1,175 citations
- Measuring the Algorithmic Efficiency of Neural Networks (2020) — 116 citations