Rohan Taori
Artificial Intelligence · 2019–2023
A researcher in the Science corpus: 7 papers; trusts 520 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 292 citations into it; #30,074 of 35,345 by trust here, #2,044 by citations
- Computer Vision and Pattern Recognition — 224 citations into it
- Evaluation & Benchmarks — 75 citations into it
- Safety, Robustness & Interpretability — 71 citations into it; #1,902 of 2,251 by trust here, #149 by citations
- Natural Language Processing — 60 citations into it
- Large Language Models — 53 citations into it
- Representation & Self-Supervised Learning — 36 citations into it
- Model Architectures — 34 citations into it
Whom Rohan Taori trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Ross B. Girshick
- Yann LeCun
- Karen Simonyan
- Jia Deng
Who trusts Rohan Taori overall
- Thomas Liao
- Nicholas Carlini
- Hieu Pham
- Mitchell Wortsman
- Anders Andreassen
- Alec Radford
- Alexander C. Li
- Maksym Andriushchenko
- Francesco Croce
- Zihang Dai
Papers from Rohan Taori's perspective
Ordered by how strongly the researchers Rohan Taori 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)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Measuring Robustness to Natural Distribution Shifts in Image Classification (2020) — 698 citations
- AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback (2023) — 921 citations
- When Robustness Doesn’t Promote Robustness: Synthetic vs. Natural Distribution Shifts on ImageNet (2019) — 21 citations
- VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use (2023) — 111 citations
- Data Feedback Loops: Model-driven Amplification of Dataset Biases (2022) — 74 citations
- Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization (2021) — 331 citations