Linjun Yang
Artificial Intelligence · 2009–2024
A researcher in the Science corpus: 6 papers; trusts 236 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 128 citations into it; #16,620 of 35,345 by trust here, #3,907 by citations
- Computer Vision and Pattern Recognition — 67 citations into it; #11,170 of 21,575 by trust here, #7,176 by citations
- Natural Language Processing — 65 citations into it
- Representation & Self-Supervised Learning — 48 citations into it
- General Social Sciences — 7 citations into it
- Multimodal Learning — 5 citations into it
Whom Linjun Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Ilya Sutskever
- Diederik P. Kingma
- Yann LeCun
- Christian Szegedy
- Ross B. Girshick
- Ruslan Salakhutdinov
Who trusts Linjun Yang overall
Papers from Linjun Yang's perspective
Ordered by how strongly the researchers Linjun Yang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- 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)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise (2017) — 515 citations
- Tag ranking (2009) — 497 citations
- Text Embeddings by Weakly-Supervised Contrastive Pre-training (2022) — 1,460 citations
- Multilingual E5 Text Embeddings: A Technical Report (2024) — 521 citations
- Improving Text Embeddings with Large Language Models (2023) — 400 citations
- Embedding-based Retrieval in Facebook Search (2020) — 410 citations