Wenjie Wang
Artificial Intelligence · 2020–2023
A researcher in the Science corpus: 3 papers; trusts 32 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 75 citations into it
- Information Systems — 54 citations into it
- Computer Vision and Pattern Recognition — 41 citations into it
- Management Science and Operations Research — 18 citations into it
- Generative Models — 15 citations into it
- Marketing — 8 citations into it
- Optimization & Training — 5 citations into it
Whom Wenjie Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Xiangnan He
- Yoshua Bengio
- Geoffrey E. Hinton
- Yehuda Koren
- Yann LeCun
- Alex Krizhevsky
- Kaiming He
- Jimmy Ba
- Ilya Sutskever
Who trusts Wenjie Wang overall
Papers from Wenjie Wang's perspective
Ordered by how strongly the researchers Wenjie Wang 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Denoising Implicit Feedback for Recommendation (2020) — 334 citations
- TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation (2023) — 782 citations
- Diffusion Recommender Model (2023) — 315 citations