Miriam Cha

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 2 papers; trusts 165 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 33 citations into it; #16,266 of 21,575 by trust here, #16,367 by citations
• Artificial Intelligence — 22 citations into it
• Generative Models — 17 citations into it; #1,522 of 1,956 by trust here, #1,766 by citations
• Multimodal Learning — 7 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Miriam Cha trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Scott E. Reed
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Christian Szegedy
7. Kaiming He
8. Jimmy Ba
9. Yann LeCun
10. Alec Radford

Who trusts Miriam Cha overall

1. Tobias Hinz
2. Chao Zhang
3. Stanislav Frolov
4. Hui Ye
5. Jiadong Liang
6. Stefan Heinrich
7. Zichao Yang
8. Xiulong Yang

Papers from Miriam Cha's perspective

Ordered by how strongly the researchers Miriam Cha 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• Adversarial Learning of Semantic Relevance in Text to Image Synthesis (2018) — 60 citations
• Multimodal Representation Learning via Maximization of Local Mutual Information (2021) — 50 citations