Nan Du
Artificial Intelligence · 2012–2023
A researcher in the Science corpus: 18 papers; trusts 382 researchers and is trusted by 38 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 698 citations into it; #7,496 of 35,345 by trust here, #459 by citations
- Natural Language Processing — 375 citations into it
- Computer Vision and Pattern Recognition — 292 citations into it; #12,712 of 21,575 by trust here, #529 by citations
- Evaluation & Benchmarks — 168 citations into it
- Large Language Models — 153 citations into it; #408 of 799 by trust here, #78 by citations
- Model Architectures — 89 citations into it; #1,745 of 2,278 by trust here, #1,038 by citations
- Efficiency & Compression — 83 citations into it
- Prompting & Reasoning — 62 citations into it; #142 of 359 by trust here, #40 by citations
Whom Nan Du trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Jure Leskovec
- Noam Shazeer
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Manuel Gomez-Rodriguez
- Alex Krizhevsky
- Alex Graves
- Yann LeCun
Who trusts Nan Du overall
- Eliav Buchnik
- Le Song
- Yi Tay
- Manuel Gomez-Rodriguez
- Rakshit S. Trivedi
- Elias Boutros Khalil
- Hanjun Dai
- Seyed Mehran Kazemi
- How Jing
- Yichen Wang
Papers from Nan Du's perspective
Ordered by how strongly the researchers Nan Du 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)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- PaLM: Scaling Language Modeling with Pathways (2022) — 8,214 citations
- Finetuned Language Models Are Zero-Shot Learners (2021) — 5,221 citations
- GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (2021) — 1,261 citations
- PaLM 2 Technical Report (2023) — 1,524 citations
- ReAct: Synergizing Reasoning and Acting in Language Models (2022) — 8,935 citations
- Designing Effective Sparse Expert Models (2022) — 118 citations
- DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining (2023) — 393 citations
- LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data (2014) — 29 citations
- Mixture-of-Experts with Expert Choice Routing (2022) — 810 citations
- ST-MoE: Designing Stable and Transferable Sparse Expert Models (2022) — 423 citations
- Joint Slot Filling and Intent Detection via Capsule Neural Networks (2018) — 253 citations
- Time-Dependent Representation for Neural Event Sequence Prediction (2017) — 64 citations
- Recurrent Marked Temporal Point Processes: Embedding Event History to Vector (2016) — 831 citations
- Time-Sensitive Recommendation From Recurrent User Activities (2015) — 137 citations
- A Deep Learning Approach to Link Prediction in Dynamic Networks (2014) — 163 citations
- Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013) — 271 citations
- Uncover Topic-Sensitive Information Diffusion Networks (2013) — 100 citations
- Learning Networks of Heterogeneous Influence (2012) — 168 citations