Joao Marques-Silva

Artificial Intelligence · 1999–2021

A researcher in the Science corpus: 11 papers; trusts 68 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 139 citations into it; #6,220 of 35,345 by trust here, #10,065 by citations
• Safety, Robustness & Interpretability — 61 citations into it; #1,643 of 2,251 by trust here, #2,041 by citations
• Computational Theory and Mathematics — 58 citations into it; #5,717 of 12,182 by trust here, #5,724 by citations
• Information Systems — 34 citations into it; #2,005 of 8,771 by trust here, #3,198 by citations
• Algorithms & Complexity — 17 citations into it; #238 of 516 by trust here, #232 by citations
• Computer Networks and Communications — 15 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Statistical Learning Theory — 13 citations into it

Whom Joao Marques-Silva trusts overall

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

1. Alexey Ignatiev
2. Cynthia Rudin
3. Himabindu Lakkaraju
4. Niklas Eén
5. Nina Narodytska
6. Andy Shih
7. Geoffrey E. Hinton
8. J. Ross Quinlan
9. Matteo Fischetti
10. Yoshua Bengio

Who trusts Joao Marques-Silva overall

1. Alexey Ignatiev
2. Tobias Achterberg
3. Martin Gebser
4. Lintao Zhang
5. Guy Katz
6. Sharad Malik
7. Zhaohui Fu
8. E. Goldberg
9. Dejan Jovanovic
10. Benjamin Kaufmann

Papers from Joao Marques-Silva's perspective

Ordered by how strongly the researchers Joao Marques-Silva 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Et al
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Conflict-Driven Clause Learning SAT Solvers (2021) — 636 citations
• Reports of the 2016 AAAI Workshop Program (2016) — 3 citations
• PySAT: A Python Toolkit for Prototyping with SAT Oracles (2018) — 346 citations
• Iterative and core-guided MaxSAT solving: A survey and assessment (2013) — 193 citations
• On Explaining Decision Trees (2020) — 116 citations
• On Relating Explanations and Adversarial Examples (2019) — 135 citations
• A SAT-Based Approach to Learn Explainable Decision Sets (2018) — 63 citations
• Learning Optimal Decision Trees with SAT (2018) — 148 citations
• Abduction-Based Explanations for Machine Learning Models (2018) — 307 citations
• Reports from the 2015 AAAI Workshop Program (2015) — 3 citations
• GRASP: A Search Algorithm for Propositional Satisfiability (1999)