Fangyu Liu
Artificial Intelligence · 2020–2024
A researcher in the Science corpus: 8 papers; trusts 502 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 223 citations into it
- Computer Vision and Pattern Recognition — 182 citations into it; #17,226 of 21,575 by trust here, #8,296 by citations
- Natural Language Processing — 108 citations into it
- Multimodal Learning — 95 citations into it
- Representation & Self-Supervised Learning — 57 citations into it
- Evaluation & Benchmarks — 53 citations into it
- Large Language Models — 43 citations into it
- Molecular Biology — 17 citations into it
Whom Fangyu Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Yann LeCun
- Ross B. Girshick
- Ilya Loshchilov
- Oriol Vinyals
Who trusts Fangyu Liu overall
- Katikapalli Subramanyam Kalyan
- Zhiyang Xu
- Michihiro Yasunaga
- Siddharth Karamcheti
- Hao Shao
- Haozhe Zhao
- Xingyu Fu
- Jun Chen
- Xikun Zhang
- Kai He
Papers from Fangyu Liu's perspective
Ordered by how strongly the researchers Fangyu Liu 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding (2022) — 473 citations
- Visual Spatial Reasoning (2022) — 352 citations
- Language Models Can See: Plugging Visual Controls in Text Generation (2022) — 120 citations
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
- Self-Alignment Pretraining for Biomedical Entity Representations (2020) — 466 citations
- PaliGemma: A versatile 3B VLM for transfer (2024) — 728 citations
- IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages (2022) — 73 citations
- Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders (2021) — 137 citations