Matteo Fischetti
Artificial Intelligence · 2000–2018
A researcher in the Science corpus: 8 papers; trusts 47 researchers and is trusted by 50 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 14 citations into it; #8,216 of 35,345 by trust here, #23,731 by citations
- Computer Networks and Communications — 11 citations into it; #2,236 of 9,886 by trust here, #4,757 by citations
- Computer Vision and Pattern Recognition — 9 citations into it; #5,934 of 21,575 by trust here, #15,529 by citations
- Computational Theory and Mathematics — 8 citations into it; #4,672 of 12,182 by trust here, #5,239 by citations
- Industrial and Manufacturing Engineering — 6 citations into it; #87 of 2,521 by trust here, #590 by citations
- Numerical Analysis — 6 citations into it; #2,648 of 3,586 by trust here, #1,659 by citations
- Control and Systems Engineering — 5 citations into it
Whom Matteo Fischetti trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Iroon Polytechniou
- Geoffrey E. Hinton
- Alex Krizhevsky
- Vinod Nair
- Yann LeCun
- Ilya Sutskever
- Yoshua Bengio
- Christian Szegedy
- Thiago Serra
- L. Eon Bottou
Who trusts Matteo Fischetti overall
- Timo Berthold
- Alexey Ignatiev
- Pierre Bonami
- Huan Zhang
- Andrea Lodi
- Hadi Salman
- Vincent Tjeng
- Marc E. Pfetsch
- Jason Jo
- Matt Jordan
Papers from Matteo Fischetti's perspective
Ordered by how strongly the researchers Matteo Fischetti trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- On handling indicator constraints in mixed integer programming (2016) — 113 citations
- Branching on nonchimerical fractionalities (2012) — 31 citations
- The feasibility pump (2005) — 455 citations
- Combinatorial Benders' Cuts for Mixed-Integer Linear Programming (2006) — 468 citations
- Algorithms for the Set Covering Problem (2000) — 478 citations
- Deep neural networks and mixed integer linear optimization (2018) — 320 citations
- Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study (2017) — 84 citations
- Local branching (2003) — 723 citations