Raphael Gontijo Lopes

Computer Vision and Pattern Recognition · with Cubuk · 2017–2022

A researcher in the Science corpus: 10 papers; trusts 935 researchers and is trusted by 117 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 322 citations into it; #3,137 of 21,575 by trust here, #1,399 by citations
• Artificial Intelligence — 289 citations into it; #8,838 of 35,345 by trust here, #1,862 by citations
• Representation & Self-Supervised Learning — 53 citations into it
• Generative Models — 52 citations into it
• Safety, Robustness & Interpretability — 52 citations into it; #993 of 2,251 by trust here, #1,063 by citations
• Evaluation & Benchmarks — 46 citations into it
• Optimization & Training — 45 citations into it
• Model Architectures — 41 citations into it

Whom Raphael Gontijo Lopes trusts overall

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

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

Who trusts Raphael Gontijo Lopes overall

1. Ekin Dogus Cubuk
2. Barret Zoph
3. Lin Wang
4. Evgenia Rusak
5. Gaurav Kumar Nayak
6. Yuang Liu
7. Jingwen Ye
8. Kartikeya Bhardwaj
9. Jogendra Nath Kundu
10. Mitchell Wortsman

Papers from Raphael Gontijo Lopes's perspective

Ordered by how strongly the researchers Raphael Gontijo Lopes 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (2022) — 8,628 citations
• A Fourier Perspective on Model Robustness in Computer Vision (2019) — 638 citations
• Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation (2019) — 229 citations
• Language Model Cascades (2022) — 119 citations
• Data-Free Knowledge Distillation for Deep Neural Networks (2017) — 305 citations
• Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation (2020) — 121 citations
• End-to-end Audio Visual Scene-aware Dialog Using Multimodal Attention-based Video Features (2018) — 132 citations
• No One Representation to Rule Them All: Overlapping Features of Training Methods (2021) — 72 citations
• Affinity and Diversity: Quantifying Mechanisms of Data Augmentation (2020) — 87 citations
• Tradeoffs in Data Augmentation: An Empirical Study (2021) — 72 citations