Sinong Wang
Artificial Intelligence · 2019–2023
A researcher in the Science corpus: 7 papers; trusts 254 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 207 citations into it; #11,329 of 35,345 by trust here, #3,261 by citations
- Natural Language Processing — 134 citations into it; #5,648 of 5,688 by trust here, #2,031 by citations
- Computer Vision and Pattern Recognition — 106 citations into it
- Large Language Models — 67 citations into it
- Model Architectures — 57 citations into it; #994 of 2,278 by trust here, #468 by citations
- Evaluation & Benchmarks — 44 citations into it
- Efficiency & Compression — 29 citations into it; #612 of 1,264 by trust here, #254 by citations
- Optimization & Training — 12 citations into it
Whom Sinong Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Richard Socher
- Kaiming He
- Yann LeCun
- Alex Graves
- Alec Radford
Who trusts Sinong Wang overall
- Yi Tay
- Markus Norman Rabe
- Tri Dao
- Shuhao Cao
- Andrew Jaegle
- Bodhisattwa Prasad Majumder
- Sachin Mehta
- Mostafa Dehghani
- Amirkeivan Mohtashami
- Gianni Brauwers
Papers from Sinong Wang's perspective
Ordered by how strongly the researchers Sinong Wang 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
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- 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
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Linformer: Self-Attention with Linear Complexity (2020) — 2,339 citations
- Entailment as Few-Shot Learner (2021) — 198 citations
- Blockwise Self-Attention for Long Document Understanding (2019) — 280 citations
- CLEAR: Contrastive Learning for Sentence Representation (2020) — 350 citations
- Language Models as Fact Checkers? (2020) — 86 citations
- Effective Long-Context Scaling of Foundation Models (2023) — 346 citations
- Luna: Linear Unified Nested Attention (2021) — 140 citations