Liangchen Luo

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 7 papers; trusts 143 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 165 citations into it; #23,276 of 35,345 by trust here, #12,974 by citations
• Computer Vision and Pattern Recognition — 129 citations into it
• Optimization & Training — 58 citations into it; #1,924 of 2,601 by trust here, #1,083 by citations
• Model Architectures — 33 citations into it
• Natural Language Processing — 20 citations into it
• Management Science and Operations Research — 16 citations into it; #10,549 of 11,946 by trust here, #3,153 by citations
• Representation & Self-Supervised Learning — 11 citations into it

Whom Liangchen Luo trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Jimmy Ba
8. Ilya Sutskever
9. Christian Szegedy
10. Sepp Hochreiter

Who trusts Liangchen Luo overall

1. Anas Barakat
2. Sharan Vaswani
3. Yuanzhi Li
4. Cyrille W. Combettes
5. Mengtian Li
6. Nicolas Loizou
7. Pedro H. P. Savarese
8. Yan Luo
9. Xinyan Zhao
10. Juntang Zhuang

Papers from Liangchen Luo's perspective

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

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

Their papers

• PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel (2023) — 795 citations
• Adaptive Gradient Methods with Dynamic Bound of Learning Rate (2019) — 693 citations
• Learning Personalized End-to-End Goal-Oriented Dialog (2018) — 87 citations
• DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction (2022) — 17 citations
• Software-hardware co-design for fast and scalable training of deep learning recommendation models (2021) — 209 citations
• High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models (2021) — 49 citations
• Large-Scale Generative Data-Free Distillation (2020) — 48 citations