Binary Distillation is a machine learning knowledge transfer framework in which a compact student model is trained to emulate the behavior of a single, more complex teacher model, distinguished by its use of soft-label supervision derived from the teacher’s output probabilities to enhance learning efficiency and performance.
149
Publications
5.5K
Citations
325
Authors
137
Institutions
Publications per year
2017–2026
21
Parents
Publications
149
1–5 of 149
Authors
325
- RW
7
h-index
- SB
6
h-index
- AW
5
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- SH
5
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- WL
4
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1–5 of 325
Institutions
137
University of Alberta
Edmonton, Canada
8 h-index
16 publications
1.9K citations
Technical University of Denmark
Kongens Lyngby, Denmark
8 h-index
21 publications
506 citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
15 publications
540 citations
Norwegian University of Science and Technology
Trondheim, Norway
5 h-index
7 publications
255 citations
University of Stuttgart
Stuttgart, Germany
4 h-index
7 publications
230 citations
Newcastle University
Newcastle upon Tyne, United Kingdom
4 h-index
7 publications
186 citations
University of California, Santa Barbara
Santa Barbara, United States
4 h-index
9 publications
147 citations
The University of Texas at Austin
Austin, United States
4 h-index
11 publications
189 citations
1–8 of 137
Venues
- Industrial & Engineering Chemistry Research
20 publications · 778 citations
16
h-index
- Chemical Engineering Science
18 publications · 1.1K citations
15
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- AIChE Journal
17 publications · 777 citations
14
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- Computers & Chemical Engineering
9 publications · 376 citations
9
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- The Canadian Journal of Chemical Engineering
5 publications · 168 citations
5
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1–5