Ricardo M. A. Silva
Artificial Intelligence · 2006–2022
A researcher in the Science corpus: 10 papers; trusts 323 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 130 citations into it; #21,829 of 35,345 by trust here, #20,377 by citations
- Safety, Robustness & Interpretability — 58 citations into it; #601 of 2,251 by trust here, #378 by citations
- Statistics and Probability — 57 citations into it
- Computer Vision and Pattern Recognition — 40 citations into it
- Sociology and Political Science — 40 citations into it
- Causal Inference & Discovery — 37 citations into it; #209 of 364 by trust here, #127 by citations
- Safety Research — 37 citations into it
- AI Ethics, Fairness & Society — 29 citations into it; #257 of 494 by trust here, #187 by citations
Whom Ricardo M. A. Silva trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Thomas S. Richardson
- Judea Pearl
- Tom L. Burr
- Geoffrey E. Hinton
- Alex Krizhevsky
- Wray L. Buntine
- Kaiming He
- Roland R. Ramsahai
- Diederik P. Kingma
- A. P. Dawid
Who trusts Ricardo M. A. Silva overall
- Benigno Uria
- Patrik O. Hoyer
- Shohei Shimizu
- Dominik Janzing
- Silvia Chiappa
- Yichuan Tang
- Kun Zhang
- Bernhard Schölkopf
- Jan Peters
- Ruslan Salakhutdinov
Papers from Ricardo M. A. Silva's perspective
Ordered by how strongly the researchers Ricardo M. A. Silva trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Mixed Cumulative Distribution Networks (2010) — 16 citations
- Gaussian Process Structural Equation Models with Latent Variables (2010) — 12 citations
- The Sensitivity of Counterfactual Fairness to Unmeasured Confounding (2019) — 65 citations
- Causal Inference through a Witness Protection Program (2014) — 21 citations
- Questions for Flat-Minima Optimization of Modern Neural Networks (2022) — 12 citations
- Making Decisions that Reduce Discriminatory Impacts (2019) — 29 citations
- Causal Reasoning for Algorithmic Fairness (2018) — 147 citations
- Counterfactual Fairness (2017) — 1,918 citations
- When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness (2017) — 180 citations
- Learning the Structure of Linear Latent Variable Models (2006) — 286 citations