Young Jin Kim

Artificial Intelligence · with Zhang · 2020–2025

A researcher in the Science corpus: 8 papers; trusts 104 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 198 citations into it; #22,121 of 35,345 by trust here, #8,077 by citations
• Computer Vision and Pattern Recognition — 83 citations into it; #13,742 of 21,575 by trust here, #8,831 by citations
• Large Language Models — 68 citations into it; #581 of 799 by trust here, #553 by citations
• Natural Language Processing — 63 citations into it; #4,051 of 5,688 by trust here, #4,032 by citations
• Evaluation & Benchmarks — 53 citations into it
• Efficiency & Compression — 40 citations into it; #690 of 1,264 by trust here, #620 by citations
• Model Architectures — 28 citations into it
• General Social Sciences — 10 citations into it

Whom Young Jin Kim trusts overall

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

1. Geoffrey E. Hinton
2. Noam Shazeer
3. Ashish Vaswani
4. Yoshua Bengio
5. Diederik P. Kingma
6. Jacob Devlin
7. Alex Krizhevsky
8. Ilya Sutskever
9. Oriol Vinyals
10. Kaiming He

Who trusts Young Jin Kim overall

1. Elias Frantar
2. Barret Zoph
3. François Lagunas
4. Mohammed Hassanin
5. Dan Alistarh
6. Samyam Rajbhandari
7. Mostafa Dehghani
8. Ella Charlaix
9. Saeed Anwar
10. William Fedus

Papers from Young Jin Kim's perspective

Ordered by how strongly the researchers Young Jin 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Scalable and Efficient MoE Training for Multitask Multilingual Models (2021) — 101 citations
• Taming Sparsely Activated Transformer with Stochastic Experts (2021) — 152 citations
• Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation (2024) — 477 citations
• Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs (2025) — 509 citations
• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone (2024) — 2,340 citations
• Mixture of Quantized Experts (MoQE): Complementary Effect of Low-bit Quantization and Robustness (2023) — 40 citations
• How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation (2023) — 603 citations
• FastFormers: Highly Efficient Transformer Models for Natural Language Understanding (2020) — 49 citations