Techniques for interpretable machine learning
Copyright policy ) Mengnan Du; Ninghao Liu; Xia Hu 0001;
Techniques for interpretable machine learning
Uncovering the mysterious ways machine learning models make decisions.
- The University of Texas System United States
- Texas A&M University United States
- Texas A&M University United States
- Texas A&M University United States
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Statistics - Machine Learning, Machine Learning (stat.ML), Machine Learning (cs.LG)
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Statistics - Machine Learning, Machine Learning (stat.ML), Machine Learning (cs.LG)
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).952 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 0.01% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 0.1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 0.01%
selected citations
Citations provided by BIP!
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP! 952
Top 0.01%
Top 0.1%
Top 0.01%
Green
bronze