Jaemin Cho

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 9 papers; trusts 1,643 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 334 citations into it; #5,246 of 21,575 by trust here, #4,402 by citations
• Artificial Intelligence — 331 citations into it; #6,130 of 35,345 by trust here, #5,424 by citations
• Natural Language Processing — 157 citations into it; #3,438 of 5,688 by trust here, #3,053 by citations
• Multimodal Learning — 133 citations into it; #266 of 1,211 by trust here, #404 by citations
• Generative Models — 79 citations into it; #1,613 of 1,956 by trust here, #1,460 by citations
• Evaluation & Benchmarks — 73 citations into it
• Large Language Models — 50 citations into it
• Representation & Self-Supervised Learning — 42 citations into it

Whom Jaemin Cho trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Krizhevsky
6. Ilya Sutskever
7. Oriol Vinyals
8. Ross B. Girshick
9. Yann LeCun
10. Christian Szegedy

Who trusts Jaemin Cho overall

1. Jianfeng Wang
2. Zhengyuan Yang
3. Zhe Gan
4. Mohit Bansal
5. Yi-Lin Sung
6. Wenliang Dai
7. Xiaowei Hu
8. Yushi Hu
9. Junnan Li
10. Jiahui Yu

Papers from Jaemin Cho's perspective

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

Their papers

• Unifying Vision-and-Language Tasks via Text Generation (2021) — 635 citations
• X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers (2020) — 109 citations
• VL-ADAPTER: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks (2021) — 499 citations
• DALL-EVAL: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models (2022) — 302 citations
• A Hierarchical Latent Structure for Variational Conversation Modeling (2018) — 98 citations
• MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding (2021) — 37 citations
• Mixture Content Selection for Diverse Sequence Generation (2019) — 64 citations
• DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers (2022) — 133 citations
• LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning (2022) — 329 citations