Daniel Coelho de Castro

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 5 papers; trusts 298 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 159 citations into it; #22,346 of 35,345 by trust here, #10,566 by citations
• Computer Vision and Pattern Recognition — 125 citations into it; #15,241 of 21,575 by trust here, #8,722 by citations
• Representation & Self-Supervised Learning — 59 citations into it
• Generative Models — 24 citations into it
• Model Architectures — 15 citations into it
• Optimization & Training — 13 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Statistics and Probability — 12 citations into it

Whom Daniel Coelho de Castro trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yann LeCun
5. Yoshua Bengio
6. Kaiming He
7. Ilya Sutskever
8. Danilo Jimenez Rezende
9. Bernhard Schölkopf
10. Radford M. Neal

Who trusts Daniel Coelho de Castro overall

1. Ishaan Gulrajani
2. Hyeonseob Nam
3. Richard J. Chen
4. David Lopez-Paz
5. Quande Liu
6. Divyat Mahajan
7. Prithvijit Chattopadhyay
8. Masoud Faraki
9. Daehee Kim
10. Lucas Mansilla

Papers from Daniel Coelho de Castro's perspective

Ordered by how strongly the researchers Daniel Coelho de Castro 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Domain Generalization via Model-Agnostic Learning of Semantic Features (2019) — 812 citations
• Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing (2022) — 489 citations
• Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing (2023) — 304 citations
• Deep Structural Causal Models for Tractable Counterfactual Inference (2020) — 331 citations
• Causality matters in medical imaging (2019) — 462 citations