Boyang Albert Li

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 5 papers; trusts 504 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 185 citations into it; #22,761 of 35,345 by trust here, #4,772 by citations
• Computer Vision and Pattern Recognition — 158 citations into it; #14,068 of 21,575 by trust here, #3,827 by citations
• Multimodal Learning — 87 citations into it
• Natural Language Processing — 73 citations into it
• Large Language Models — 65 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Medical Imaging — 5 citations into it

Whom Boyang Albert Li trusts overall

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

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

Who trusts Boyang Albert Li overall

1. David R Ha
2. J. Urgen Schmidhuber

Papers from Boyang Albert Li's perspective

Ordered by how strongly the researchers Boyang Albert Li 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. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning (2023) — 3,705 citations
• VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning (2021) — 299 citations
• Game Engine Learning from Video (2017) — 51 citations
• Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training (2022) — 141 citations
• Is GPT-3 a Good Data Annotator? (2022) — 353 citations