Context-sensitive Transfer Functions
Context-Sensitive Transfer Functions is a transfer function design method that adapts its transformation behavior based on the surrounding or preceding computational, structural, or semantic context, thereby distinguishing itself from context-insensitive transfer functions by dynamically modulating output according to environmental or conditional input features.
9
Publications
655
Citations
38
Authors
15
Institutions
Publications per year
2017–2026
2
Parents
9
Modular interprocedural pointer analysis using access paths
Ben-Chung Cheng, Wen‐mei Hwu · 2000 · 166 citations
Inductive transfer with context-sensitive neural networks
Daniel Silver, Ryan Poirier, Duane Currie · Machine Learning · 2008 · 50 citations · Full text
Modular interprocedural pointer analysis using access paths
Ben-Chung Cheng, Wen‐mei Hwu · ACM SIGPLAN Notices · 2000 · 43 citations · Full text
1–5 of 9
38
2
h-index
2
h-index
1
h-index
1
h-index
1
h-index
1–5 of 38
15
University of Illinois Urbana-Champaign
Urbana, United States
2 h-index
4 publications
418 citations

Acadia University
Wolfville, Canada
1 h-index
3 publications
150 citations
Pearson (United Kingdom)
London, United Kingdom
1 h-index
1 publications
250 citations

University of Vermont
Burlington, United States
1 h-index
2 publications
76 citations
Honda (Germany)
Frankfurt am Main, Germany
1 h-index
6 publications
234 citations
Universidade Federal de São Carlos
São Carlos, Brazil
1 h-index
2 publications
50 citations
Bjerknes Centre for Climate Research
Bergen, Norway
1 h-index
2 publications
500 citations
Bielefeld University
Bielefeld, Germany
1 h-index
4 publications
156 citations
1–8 of 15
1 publications · 39 citations
1
h-index
1 publications · 43 citations
1
h-index
1 publications · 25 citations
1
h-index
1 publications · 30 citations
1
h-index
1 publications · 38 citations
1
h-index
1–5