Nathanael Scharli
Artificial Intelligence · 2002–2024
A researcher in the Science corpus: 8 papers; trusts 66 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 234 citations into it; #19,825 of 35,345 by trust here, #2,409 by citations
- Natural Language Processing — 119 citations into it
- Computer Vision and Pattern Recognition — 76 citations into it
- Prompting & Reasoning — 76 citations into it; #248 of 359 by trust here, #99 by citations
- Information Systems — 51 citations into it
- Code Generation & Program Synthesis — 14 citations into it
- Alignment & RLHF — 11 citations into it
Whom Nathanael Scharli trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Graves
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Sepp Hochreiter
- Yann LeCun
- Kaiming He
- J. Urgen Schmidhuber
Who trusts Nathanael Scharli overall
- Jie Huang
- Xinyun Chen
- Wenhu Chen
- Jason Wei
- Najoung Kim
- Michihiro Yasunaga
- Denny Zhou
- Shibo Hao
- Kevin Chen-Chuan Chang
- Chengrun Yang
Papers from Nathanael Scharli's perspective
Ordered by how strongly the researchers Nathanael Scharli trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- 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)
Their papers
- Least-to-Most Prompting Enables Complex Reasoning in Large Language Models (2022) — 1,834 citations
- Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them (2022) — 2,044 citations
- Measuring Compositional Generalization: A Comprehensive Method on Realistic Data (2019) — 421 citations
- Compositional Semantic Parsing with Large Language Models (2022) — 113 citations
- Teaching Large Language Models to Self-Debug (2023) — 1,232 citations
- Large Language Models Can Be Easily Distracted by Irrelevant Context (2023) — 1,096 citations
- When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
- Traits: Composable Units of Behaviour (2002) — 463 citations