Shaojie Bai
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 7 papers; trusts 937 researchers and is trusted by 72 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 214 citations into it; #3,775 of 35,345 by trust here, #3,457 by citations
- Model Architectures — 156 citations into it; #303 of 2,278 by trust here, #373 by citations
- Computer Vision and Pattern Recognition — 144 citations into it; #6,853 of 21,575 by trust here, #7,249 by citations
- Natural Language Processing — 98 citations into it
- Optimization & Training — 44 citations into it
- Signal Processing — 31 citations into it; #930 of 7,572 by trust here, #456 by citations
- Large Language Models — 17 citations into it
- Generative Models — 14 citations into it
Whom Shaojie Bai trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Diederik P. Kingma
- Yann LeCun
- Kaiming He
- Alex Graves
- Sepp Hochreiter
- J. Urgen Schmidhuber
Who trusts Shaojie Bai overall
- Vladlen Koltun
- Guohao Li
- Chien-Yao Wang
- Albert Gu
- Zachary Teed
- Yao-Hung Hubert Tsai
- Samy Wu Fung
- Ailing Zeng
- Yi Luo
- Antonino Furnari
Papers from Shaojie Bai's perspective
Ordered by how strongly the researchers Shaojie Bai 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- 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)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Deep Equilibrium Models (2019) — 942 citations
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling (2018) — 6,867 citations
- Multimodal Transformer for Unaligned Multimodal Language Sequences (2019) — 2,255 citations
- Trellis Networks for Sequence Modeling (2018) — 165 citations
- Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel (2019) — 333 citations
- Multiscale Deep Equilibrium Models (2020) — 259 citations
- Convolutional Sequence Modeling Revisited (2018) — 78 citations