Inner Product Function
The Inner Product Function is a fundamental boolean function in communication complexity that computes the parity of the bitwise dot product of two binary input vectors, distinguishing itself from other boolean functions by its reliance on modular arithmetic over the finite field GF(2) to determine output.
242
Publications
11.5K
Citations
447
Authors
249
Institutions
Publications per year
2017–2026
33
Variants
Ip Function
Parents
242
Boosting Algorithms as Gradient Descent
Llew Mason, Jonathan Baxter, Peter L. Bartlett et al. · 1999 · 712 citations
Text Classification using String Kernels
Huma Lodhi, John Shawe‐Taylor, Nello Cristianini et al. · Bristol Research (University of Bristol) · 2000 · 514 citations · Full text
PARTIAL DISTANCE CORRELATION WITH METHODS FOR DISSIMILARITIES
Maria L. Rizzo · 2016 · 303 citations
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249
Texas A&M University
College Station, United States
4 h-index
8 publications
765 citations
Nanyang Technological University
Singapore, Singapore
4 h-index
5 publications
157 citations
University of California, Santa Barbara
Santa Barbara, United States
4 h-index
5 publications
245 citations

Northeastern University
Shenyang, China
4 h-index
9 publications
123 citations
University of California, Los Angeles
Los Angeles, United States
3 h-index
5 publications
339 citations

Xi'an Jiaotong University
Xi'an, China
3 h-index
5 publications
238 citations

University of Waterloo
Waterloo, Canada
3 h-index
3 publications
87 citations
Stanford University
Stanford, United States
3 h-index
5 publications
555 citations
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7 publications · 366 citations
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Repository · 5 publications · 491 citations
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Repository · 4 publications · 138 citations
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4 publications · 563 citations
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IJM · 4 publications · 62 citations
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