M. Schwartz
Nuclear and High Energy Physics · with Izaguirre · 2001–2018
A researcher in the Science corpus: 14 papers; trusts 69 researchers and is trusted by 57 researchers here, as inferred from the citation graph.
Subfields
- Nuclear and High Energy Physics — 267 citations into it; #295 of 6,161 by trust here, #709 by citations
- Artificial Intelligence — 39 citations into it; #12,444 of 35,345 by trust here, #18,186 by citations
- Astronomy and Astrophysics — 25 citations into it; #1,967 of 10,964 by trust here, #3,991 by citations
- AI for Physical Sciences — 23 citations into it
- Natural Language Processing — 18 citations into it
- Computer Vision and Pattern Recognition — 17 citations into it; #9,250 of 21,575 by trust here, #14,478 by citations
- Model Architectures — 12 citations into it
- Computer Networks and Communications — 6 citations into it
Whom M. Schwartz trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- T. Becher
- Matteo Cacciari
- S. Fleming
- M. Neubert
- G. Salam
- Geoffrey E. Hinton
- Christian W. Bauer
- Yoshua Bengio
- Jesse Thaler
- A. Larkoski
Who trusts M. Schwartz overall
- A. Larkoski
- T. Becher
- E. Pajer
- I. Moult
- Patrick T. Komiske
- A. Broggio
- Benjamin Nachman
- Y. Chien
- S. D. Ellis
- Iain W. Stewart
Papers from M. Schwartz's perspective
Ordered by how strongly the researchers M. Schwartz trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Going deeper with convolutions
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
Their papers
- Jet substructure at the Tevatron and LHC: new results, new tools, new benchmarksReport prepared by the participants of the BOOST 2011 Workshop at Princeton University, 22–26 May 2011. L Asquith (lasquith@hep.anl.gov), S Rappoccio (rappocc@fnal.gov) and C K Vermilion (verm@uw.edu), editors. (2011) — 290 citations
- JUNIPR: a framework for unsupervised machine learning in particle physics (2018) — 146 citations
- Deep learning in color: towards automated quark/gluon jet discrimination (2016) — 279 citations
- Factorization for groomed jet substructure beyond the next-to-leading logarithm (2016) — 162 citations
- Quantum Field Theory and the Standard Model (2013) — 1,095 citations
- Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012 (2013) — 200 citations
- Quark and gluon jet substructure (2012) — 149 citations
- The two-loop hemisphere soft function (2011) — 153 citations
- Simplified Models for LHC New Physics Searches (2011) — 652 citations
- Resummation of heavy jet mass and comparison to LEP data (2010) — 132 citations
- A Precise Determination of \(\alpha_s\) From LEP Thrust Data Using Effective Field Theory (2008) — 226 citations
- Resummation and NLO matching of event shapes with effective field theory (2007) — 145 citations
- Effective field theory for massive gravitons and gravity in theory space (2002) — 759 citations
- Quantum field theory and unification in AdS5 (2001) — 181 citations