Sumanth Dathathri
Artificial Intelligence · 2019–2022
A researcher in the Science corpus: 6 papers; trusts 115 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 257 citations into it; #14,342 of 35,345 by trust here, #1,560 by citations
- Natural Language Processing — 184 citations into it; #2,768 of 5,688 by trust here, #743 by citations
- Large Language Models — 59 citations into it
- Computer Vision and Pattern Recognition — 49 citations into it; #14,569 of 21,575 by trust here, #4,174 by citations
- Evaluation & Benchmarks — 36 citations into it
- Reinforcement Learning — 13 citations into it
Whom Sumanth Dathathri trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Ronald J. Williams
- Alec Radford
- Ilya Sutskever
- Diederik P. Kingma
- Richard Socher
- Alex Krizhevsky
- Yann LeCun
- Alex Graves
Who trusts Sumanth Dathathri overall
- Sachin Kumar
- Emily Sheng
- Ruibo Liu
- Tomasz Korbak
- Emily Dinan
- Ximing Lu
- Alisa Liu
- Suchin Gururangan
- Kai-Wei Chang
- Andrea Madotto
Papers from Sumanth Dathathri's perspective
Ordered by how strongly the researchers Sumanth Dathathri trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
- Plug and Play Language Models: A Simple Approach to Controlled Text Generation (2019) — 1,229 citations
- Improving alignment of dialogue agents via targeted human judgements (2022) — 676 citations
- Challenges in Detoxifying Language Models (2021) — 248 citations
- Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models (2022) — 69 citations
- Plug-and-Play Conversational Models (2020) — 58 citations