Xiaolong Huang

Artificial Intelligence · 2022–2025

A researcher in the Science corpus: 4 papers; trusts 23 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #25,603 of 35,345 by trust here, #4,577 by citations
• Natural Language Processing — 61 citations into it
• Representation & Self-Supervised Learning — 36 citations into it

Whom Xiaolong Huang trusts overall

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

1. Geoffrey E. Hinton
2. Alexis Conneau
3. Yoshua Bengio
4. Kaiming He
5. Nils Reimers
6. Diederik P. Kingma
7. Ilya Sutskever
8. Alex Krizhevsky
9. Jacob Devlin
10. Yann LeCun

Who trusts Xiaolong Huang overall

1. Hong-Liu Cao
2. Luke Merrick
3. Zach Nussbaum
4. Max Vargas
5. Jean Kaddour

Papers from Xiaolong Huang's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
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

• Text Embeddings by Weakly-Supervised Contrastive Pre-training (2022) — 1,460 citations
• Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso (2025)
• Multilingual E5 Text Embeddings: A Technical Report (2024) — 521 citations
• Improving Text Embeddings with Large Language Models (2023) — 400 citations