Haoming Jiang

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 6 papers; trusts 179 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 224 citations into it; #15,042 of 35,345 by trust here, #4,860 by citations
• Natural Language Processing — 124 citations into it
• Computer Vision and Pattern Recognition — 94 citations into it; #9,735 of 21,575 by trust here, #4,001 by citations
• Large Language Models — 69 citations into it
• Model Architectures — 31 citations into it
• Optimization & Training — 30 citations into it; #1,463 of 2,601 by trust here, #525 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Management Science and Operations Research — 10 citations into it

Whom Haoming Jiang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Jimmy Ba
6. Ilya Sutskever
7. Kaiming He
8. Jacob Devlin
9. Ashish Vaswani
10. Yann LeCun

Who trusts Haoming Jiang overall

1. Xiaodong Liu
2. Zhe Gan
3. Hao Cheng
4. Boxin Wang
5. Zonghan Yang
6. Pengcheng He
7. Jonathan Pilault
8. Markus Bayer
9. Jianfeng Gao
10. Simiao Zuo

Papers from Haoming Jiang's perspective

Ordered by how strongly the researchers Haoming Jiang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• On the Variance of the Adaptive Learning Rate and Beyond (2019) — 2,272 citations
• SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization (2019) — 613 citations
• SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models (2022) — 59 citations
• Transformer Hawkes Process (2020) — 399 citations
• Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond (2023) — 1,077 citations
• Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing (2019) — 37 citations