Marc E. Pfetsch
Artificial Intelligence · 2006–2020
A researcher in the Science corpus: 6 papers; trusts 40 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 19 citations into it; #18,261 of 35,345 by trust here, #34,213 by citations
- Computer Networks and Communications — 18 citations into it; #4,484 of 9,886 by trust here, #9,518 by citations
- Computational Mechanics — 17 citations into it; #6,083 of 10,001 by trust here, #7,501 by citations
- Computational Theory and Mathematics — 14 citations into it; #4,694 of 12,182 by trust here, #10,349 by citations
- Industrial and Manufacturing Engineering — 11 citations into it; #1,470 of 2,521 by trust here, #2,060 by citations
- Algorithms & Complexity — 7 citations into it
- Computer Vision and Pattern Recognition — 7 citations into it
- Optimization & Training — 7 citations into it
Whom Marc E. Pfetsch trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Dimitris J. Bertsimas
- Noam Goldberg
- Robert E. Schapire
- Leo Breiman
- Matteo Fischetti
- Philip M. Long
- Jacques Desrosiers
- Jonathan Eckstein
- Trevor J. Hastie
- Joel A. Tropp
Who trusts Marc E. Pfetsch overall
- Emilio Carrizosa
- S. Ilker Birbil
- François Margot
- Cristina Molero-Río
- Mert Edali
- Afonso S. Bandeira
- Dolores Romero Morales
- Birol Yüceoglu
Papers from Marc E. Pfetsch's perspective
Ordered by how strongly the researchers Marc E. Pfetsch 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Regression Shrinkage and Selection via the Lasso
- A decision-theoretic generalization of on-line learning and an application to boosting
- The Nature of Statistical Learning Theory
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Atomic Decomposition by Basis Pursuit
Their papers
- Packing and partitioning orbitopes (2006) — 140 citations
- IPBoost - Non-Convex Boosting via Integer Programming (2020) — 6 citations
- The SCIP Optimization Suite 7.0 (2020) — 650 citations
- The SCIP Optimization Suite 3.2 (2016) — 100 citations
- The Computational Complexity of the Restricted Isometry Property, the Nullspace Property, and Related Concepts in Compressed Sensing (2012) — 403 citations
- A Column-Generation Approach to Line Planning in Public Transport (2007) — 322 citations