Beidi Chen

Artificial Intelligence · International Conference on Machine Learning · 2019–2024

A researcher in the Science corpus: 9 papers; trusts 395 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 342 citations into it; #26,252 of 35,345 by trust here, #8,525 by citations
• Computer Vision and Pattern Recognition — 242 citations into it; #16,093 of 21,575 by trust here, #10,500 by citations
• Efficiency & Compression — 136 citations into it
• Model Architectures — 107 citations into it; #2,159 of 2,278 by trust here, #1,927 by citations
• Large Language Models — 89 citations into it
• Natural Language Processing — 59 citations into it
• Evaluation & Benchmarks — 48 citations into it
• Optimization & Training — 48 citations into it

Whom Beidi Chen trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ilya Sutskever
7. Yann LeCun
8. Karen Simonyan
9. Jia Deng
10. Ross B. Girshick

Who trusts Beidi Chen overall

1. Tri Dao
2. Daniel Y. Fu
3. Zichang Liu
4. Weiyang Liu
5. L. Yu
6. Sangho Lee
7. Atri Rudra
8. Christopher R'e
9. Stefano Ermon
10. Randall Balestriero

Papers from Beidi Chen's perspective

Ordered by how strongly the researchers Beidi Chen 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Angular Visual Hardness (2019) — 60 citations
• Scatterbrain: Unifying Sparse and Low-rank Attention Approximation (2021) — 175 citations
• Efficient Streaming Language Models with Attention Sinks (2023) — 2,131 citations
• Monarch: Expressive Structured Matrices for Efficient and Accurate Training (2022) — 133 citations
• High-throughput Generative Inference of Large Language Models with a Single GPU (2023) — 797 citations
• Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models (2021) — 95 citations
• Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time (2023) — 356 citations
• GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection (2024) — 500 citations
• H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models (2023) — 817 citations