scholarly journals Ternary codes, biplanes, and the nonexistence of some quasisymmetric and quasi‐3 designs

2020 ◽  
Vol 28 (10) ◽  
pp. 745-752
Author(s):  
Akihiro Munemasa ◽  
Vladimir D. Tonchev
Keyword(s):  
Ternary Codes

scholarly journals Ternary codes and Jacobi forms

2004 ◽  
Vol 282 (1-3) ◽  
pp. 81-87 ◽  
Author(s):  
YoungJu Choie ◽  
Patrick Solé
Keyword(s):  
Jacobi Forms ◽  
Ternary Codes

Binary Views of Ternary Codes

1981 ◽  
pp. 593-598
Author(s):  
Harold N. Ward
Keyword(s):  
Ternary Codes

scholarly journals Deep Learning to Ternary Hash Codes by Continuation

2021 ◽  
Author(s):  
Mingrui Chen ◽  
Weiyu Li ◽  
weizhi lu
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Continuation Method ◽  
Binary Codes ◽  
Hard Thresholding ◽  
Joint Learning ◽  
Ternary Codes ◽  
First Time ◽  
Hash Codes ◽  
Discrete Functions

Recently, it has been observed that $\{0,\pm1\}$-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform $\{-1, 1\}$-binary codes in image retrieval. To obtain better ternary codes, we for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non-smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes.

scholarly journals Deep Learning to Ternary Hash Codes by Continuation

2021 ◽  
Author(s):  
Mingrui Chen ◽  
Weiyu Li ◽  
weizhi lu
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Continuation Method ◽  
Binary Codes ◽  
Hard Thresholding ◽  
Joint Learning ◽  
Ternary Codes ◽  
First Time ◽  
Hash Codes ◽  
Discrete Functions

Recently, it has been observed that $\{0,\pm1\}$-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform $\{-1, 1\}$-binary codes in image retrieval. To obtain better ternary codes, we for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non-smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes.

scholarly journals Semidefinite bounds for mixed binary/ternary codes

2018 ◽  
Vol 341 (6) ◽  
pp. 1740-1748 ◽  
Author(s):  
Bart Litjens
Keyword(s):  
Ternary Codes

A Novel Assessment of Baroreflex Activity Through the Similarity of Ternary Codes of Oscillations Between Arterial Blood Pressure and R–R Intervals

2020 ◽  
Vol 40 (5) ◽  
pp. 727-734
Author(s):  
Po-Wei Chen ◽  
Chun-Keng Lin ◽  
Wei-Min Liu ◽  
Yu-Chen Chen ◽  
Sheng-Tzung Tsai ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Arterial Blood Pressure ◽  
Arterial Blood ◽  
Ternary Codes
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