John Arevalo

Computer Vision and Pattern Recognition · 2013–2017

A researcher in the Science corpus: 3 papers; trusts 121 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it; #19,414 of 21,575 by trust here, #6,873 by citations
• Artificial Intelligence — 32 citations into it; #31,600 of 35,345 by trust here, #9,760 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Multimodal Learning — 13 citations into it; #1,058 of 1,211 by trust here, #962 by citations
• Natural Language Processing — 8 citations into it
• Model Architectures — 7 citations into it
• Signal Processing — 7 citations into it
• Optimization & Training — 6 citations into it

Whom John Arevalo trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yann LeCun
6. Richard Socher
7. Ruslan Salakhutdinov
8. Ilya Sutskever
9. Karen Simonyan
10. Tomas Mikolov

Who trusts John Arevalo overall

1. Douwe Kiela
2. Amanpreet Singh
3. S. Suganyadevi
4. Chao Zhang
5. Xiang Bai
6. Mufti Mahmud
7. Deli Yu
8. Mengmeng Ma
9. Mugahed A. Al-antari
10. Richard J. Chen

Papers from John Arevalo's perspective

Ordered by how strongly the researchers John Arevalo 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. 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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Gated Multimodal Units for Information Fusion (2017) — 625 citations
• A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection (2013) — 389 citations
• Representation learning for mammography mass lesion classification with convolutional neural networks (2016) — 443 citations