Liangzhen Lai

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 6 papers; trusts 163 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 116 citations into it; #18,880 of 35,345 by trust here, #5,153 by citations
• Computer Vision and Pattern Recognition — 103 citations into it
• Model Architectures — 39 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Signal Processing — 12 citations into it; #5,224 of 7,572 by trust here, #4,201 by citations

Whom Liangzhen Lai trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Yundong Zhang
6. Ilya Sutskever
7. Kaiming He
8. Andrew G. Howard
9. Yangqing Jia
10. Diederik P. Kingma

Who trusts Liangzhen Lai overall

1. Pete Warden
2. Manuele Rusci
3. Ji Lin
4. Md Maruf Hossain Shuvo
5. Sehoon Kim
6. Alessandro Capotondi
7. Daniele Palossi
8. Leonardo Ravaglia
9. Colby R. Banbury
10. Luca Benini

Papers from Liangzhen Lai's perspective

Ordered by how strongly the researchers Liangzhen Lai trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network (2017) — 561 citations
• Hello Edge: Keyword Spotting on Microcontrollers (2017) — 505 citations
• Federated Learning with Non-IID Data (2018) — 3,343 citations
• LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding (2024) — 296 citations
• Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation (2021) — 245 citations
• CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs (2018) — 489 citations