Juan C. Caicedo

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 9 papers; trusts 772 researchers and is trusted by 621 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 166 citations into it; #1,732 of 21,575 by trust here, #2,401 by citations
• Biophysics — 107 citations into it; #910 of 1,196 by trust here, #240 by citations
• Artificial Intelligence — 85 citations into it; #3,498 of 35,345 by trust here, #3,146 by citations
• Molecular Biology — 80 citations into it; #16,668 of 17,773 by trust here, #6,746 by citations
• Media Technology — 58 citations into it
• Medical Imaging — 46 citations into it
• Evaluation & Benchmarks — 30 citations into it
• Multimodal Learning — 30 citations into it; #250 of 1,211 by trust here, #259 by citations

Whom Juan C. Caicedo trusts overall

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

1. Ross B. Girshick
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Christian Szegedy
6. Karen Simonyan
7. Yoshua Bengio
8. Yann LeCun
9. Andrew Zisserman
10. Diederik P. Kingma

Who trusts Juan C. Caicedo overall

1. Spyros Gidaris
2. Dingwen Zhang
3. Liang-Chieh Chen
4. Yongxi Lu
5. Stefan Mathe
6. Sean Welleck
7. Miriam Bellver
8. Sangdoo Yun
9. Joel Stehouwer
10. Gabriel Maicas

Papers from Juan C. Caicedo's perspective

Ordered by how strongly the researchers Juan C. Caicedo trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models (2015) — 2,641 citations
• Active Object Localization with Deep Reinforcement Learning (2015) — 468 citations
• Data-analysis strategies for image-based cell profiling (2017) — 696 citations
• CellProfiler 3.0: Next-generation image processing for biology (2018) — 1,741 citations
• Applications in image-based profiling of perturbations. (2016) — 160 citations
• Nucleus segmentation across imaging experiments: The 2018 Data Science Bowl (2019) — 781 citations
• Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images (2018) — 308 citations
• CytoGAN: Generative Modeling of Cell Images (2017) — 72 citations
• Automating Morphological Profiling with Generic Deep Convolutional Networks (2016) — 90 citations