James H. Faghmous
Global and Planetary Change · 2012–2016
A researcher in the Science corpus: 9 papers; trusts 8 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Global and Planetary Change — 61 citations into it; #4,653 of 10,225 by trust here, #4,939 by citations
- Atmospheric Science — 38 citations into it; #3,803 of 4,804 by trust here, #2,974 by citations
- Oceanography — 34 citations into it; #3,229 of 4,047 by trust here, #2,772 by citations
- Artificial Intelligence — 28 citations into it; #21,325 of 35,345 by trust here, #10,798 by citations
- Statistics and Probability — 25 citations into it; #12,233 of 12,932 by trust here, #6,552 by citations
- Control and Systems Engineering — 7 citations into it
- Computational Theory and Mathematics — 6 citations into it
Whom James H. Faghmous trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Dudley Chelton
- M. Schlax
- Roger Samelson
- Kerry A. Emanuel
- Pat Langley
- Andrew J. Majda
- Justin Sheffield
- Soumyadeep Chatterjee
- Vipin Kumar
- Eric R. Ziegel
Who trusts James H. Faghmous overall
- Anuj Karpatne
- Jared D. Willard
- Gowtham Atluri
- Vipin Kumar
- Michael S. Steinbach
- Jinsong Wu
- Xiaowei Jia
- I. Ebert‐Uphoff
- Yazhou Xie
Papers from James H. Faghmous's perspective
Ordered by how strongly the researchers James H. Faghmous trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Global observations of large oceanic eddies
- Global observations of nonlinear mesoscale eddies
- Model selection and estimation in regression with grouped variables
- The Elements of Statistical Learning
- Recurrent neural network based language model
- Controlling the false discovery rate: a practical and powerful approach to multiple testing
- MapReduce: simplified data processing on large clusters
- The end of model democracy?
- Statistical learning theory
Their papers
- Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016) — 1,347 citations
- A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) — 212 citations
- Global Monitoring of Inland Water Dynamics: State-of-the-Art, Challenges, and Opportunities (2016) — 82 citations
- A daily global mesoscale ocean eddy dataset from satellite altimetry (2015) — 322 citations
- Computing and Climate (2015) — 6 citations
- Spatio-Temporal Consistency as a Means to Identify Unlabeled Objects in a Continuous Data Field (2014) — 6 citations
- Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014)
- Theory-Guided Data Science for Climate Change (2014) — 33 citations
- EddyScan: A physically consistent ocean eddy monitoring application (2012) — 28 citations