Sehoon Kim

Artificial Intelligence · 2021–2024

A researcher in the Science corpus: 6 papers; trusts 301 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 340 citations into it; #22,718 of 35,345 by trust here, #10,667 by citations
• Computer Vision and Pattern Recognition — 268 citations into it; #14,331 of 21,575 by trust here, #8,205 by citations
• Efficiency & Compression — 245 citations into it; #807 of 1,264 by trust here, #685 by citations
• Model Architectures — 92 citations into it
• Large Language Models — 91 citations into it
• Natural Language Processing — 64 citations into it
• Optimization & Training — 29 citations into it
• Evaluation & Benchmarks — 18 citations into it; #2,487 of 3,147 by trust here, #2,133 by citations

Whom Sehoon Kim trusts overall

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

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

Who trusts Sehoon Kim overall

1. Elias Frantar
2. Yuhui Li
3. Coleman Hooper
4. Jing Liu
5. Yelysei Bondarenko
6. Qing Jin
7. Xiaoxia Wu
8. Zi-Mei Yao
9. A. Gholami
10. Albert Tseng

Papers from Sehoon Kim's perspective

Ordered by how strongly the researchers Sehoon Kim 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Learned Token Pruning for Transformers (2021) — 225 citations
• I-BERT: Integer-only BERT Quantization (2021) — 459 citations
• A Survey of Quantization Methods for Efficient Neural Network Inference (2021) — 1,633 citations
• KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization (2024) — 589 citations
• SqueezeLLM: Dense-and-Sparse Quantization (2023) — 345 citations
• Big Little Transformer Decoder (2023) — 24 citations