Juan Pablo Vielma

Artificial Intelligence · 2008–2022

A researcher in the Science corpus: 6 papers; trusts 28 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it
• Computational Theory and Mathematics — 29 citations into it; #6,876 of 12,182 by trust here, #8,294 by citations
• Numerical Analysis — 23 citations into it; #874 of 3,586 by trust here, #1,559 by citations
• Computer Vision and Pattern Recognition — 19 citations into it
• Safety, Robustness & Interpretability — 16 citations into it
• Computer Networks and Communications — 10 citations into it; #5,230 of 9,886 by trust here, #7,492 by citations
• Industrial and Manufacturing Engineering — 9 citations into it; #275 of 2,521 by trust here, #1,449 by citations
• Control and Systems Engineering — 8 citations into it; #7,669 of 8,257 by trust here, #6,417 by citations

Whom Juan Pablo Vielma trusts overall

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

1. Alexander Schrijver
2. George L. Nemhauser
3. Keith Ball
4. Volker Kaibel
5. Dimitris J. Bertsimas
6. Tobias Achterberg
7. Aharon Ben-Tal
8. S. Burer
9. Karla L. Hoffman
10. Walter Briec

Who trusts Juan Pablo Vielma overall

1. Carlos M. Correa-Posada
2. Velibor V. Misic
3. George L. Nemhauser
4. David Z. W. Wang
5. Pietro Belotti
6. Vincent Tjeng
7. Ross Anderson
8. Rahul Mazumder
9. Shabbir Ahmed
10. Haoxiang Liu

Papers from Juan Pablo Vielma's perspective

Ordered by how strongly the researchers Juan Pablo Vielma trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Et al
6. Intriguing properties of neural networks
7. Theory of linear and integer programming
8. GradientBased Learning Applied to Document Recognition
9. Long Short-Term Memory
10. Linear Programming and Extensions

Their papers

• Strong mixed-integer programming formulations for trained neural networks (2018) — 321 citations
• Modeling disjunctive constraints with a logarithmic number of binary variables and constraints (2008) — 342 citations
• JuMP 1.0: recent improvements to a modeling language for mathematical optimization (2022) — 450 citations
• Polyhedral approximation in mixed-integer convex optimization (2016) — 64 citations
• Mixed Integer Linear Programming Formulation Techniques (2015) — 379 citations
• Mixed-Integer Models for Nonseparable Piecewise-Linear Optimization: Unifying Framework and Extensions (2010) — 399 citations