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AntMan: Sparse Low-Rank Compression to Accelerate RNN inference
[article]
2019
arXiv
pre-print
Preserved Fulltext
To address this issue, we develop AntMan, combining structured sparsity with low-rank decomposition synergistically, to reduce model computation, size and execution time of RNNs while attaining desired ...
AntMan extends knowledge distillation based training to learn the compressed models efficiently. ...
Conclusion We develop AntMan, a technique that combines structured sparsity and low-rank decomposition to compress dense matrix-multiplications. ...
arXiv:1910.01740v1
fatcat:cs2fz54ncnft3pay3ckseuy3cm
ADVCOMP 2014 Technical Program Committee
2014
ADVCOMP
unpublished
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
We gratefully appreciate to the technical program committee co-chairs that contributed to identify the appropriate groups to submit contributions. ...
We also kindly thank all the authors that dedicated much of their time and efforts to contribute to ADVCOMP 2014. ...
(r j ) is the No. j correct result's ranking position, so the average ranking value is calculated as follows: Average-r = 1/m m rank(r j ) (8) j=1 This value reflects the average ranking of query in ...
fatcat:6j6ehjqydndxfn4bbijomtbefi