Bin Cui
Artificial Intelligence · 2012–2024
A researcher in the Science corpus: 10 papers; trusts 508 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 457 citations into it; #26,827 of 35,345 by trust here, #12,083 by citations
- Computer Vision and Pattern Recognition — 362 citations into it; #18,619 of 21,575 by trust here, #11,083 by citations
- Information Systems — 174 citations into it; #4,232 of 8,771 by trust here, #1,867 by citations
- Generative Models — 159 citations into it
- Signal Processing — 65 citations into it; #7,038 of 7,572 by trust here, #5,086 by citations
- Statistical and Nonlinear Physics — 62 citations into it
- Natural Language Processing — 57 citations into it
- Large Language Models — 54 citations into it
Whom Bin Cui trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- L. Eon Bottou
- Diederik P. Kingma
- Yoshua Bengio
- Yehuda Koren
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Ilya Sutskever
- Ruslan Salakhutdinov
Who trusts Bin Cui overall
Papers from Bin Cui's perspective
Ordered by how strongly the researchers Bin Cui 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
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Diffusion Models: A Comprehensive Survey of Methods and Applications (2022) — 2,329 citations
- Contrastive Learning for Sequential Recommendation (2022) — 559 citations
- Challenging the Long Tail Recommendation (2012) — 366 citations
- Retrieval-Augmented Generation for AI-Generated Content: A Survey (2024) — 644 citations
- Dense-to-Sparse Gate for Mixture-of-Experts (2021) — 36 citations
- PointCLIP: Point Cloud Understanding by CLIP (2021) — 671 citations
- Reliable Data Distillation on Graph Convolutional Network (2020) — 102 citations
- Graph Neural Networks in Recommender Systems: A Survey (2020) — 1,821 citations
- Heterogeneity-aware Distributed Parameter Servers (2017) — 219 citations
- LCARS: a location-content-aware recommender system (2013) — 377 citations