Hongjin Su
Artificial Intelligence · 2022–2024
A researcher in the Science corpus: 3 papers; trusts 19 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 97 citations into it
- Natural Language Processing — 57 citations into it
- Computer Vision and Pattern Recognition — 39 citations into it
- Representation & Self-Supervised Learning — 14 citations into it; #3,044 of 3,145 by trust here, #2,178 by citations
Whom Hongjin Su trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Yann LeCun
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- L. V. D. van der Maaten
- Alex Graves
- Nils Reimers
Who trusts Hongjin Su overall
- Zach Nussbaum
- Shitao Xiao
- Hong-Liu Cao
- Weijia Shi
- Yushi Hu
- Zhuosheng Zhang
- Brandon Duderstadt
- Zheng Liu
- Parishad BehnamGhader
- Zehan Li
Papers from Hongjin Su's perspective
Ordered by how strongly the researchers Hongjin Su 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- 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
- Selective Annotation Makes Language Models Better Few-Shot Learners (2022) — 329 citations
- One Embedder, Any Task: Instruction-Finetuned Text Embeddings (2022) — 475 citations
- Generative Representational Instruction Tuning (2024)