Improved Trainable Calibration Method for Neural Networks.

Gongbo Liang; Yu Zhang; Xiaoqin Wang; Nathan Jacobs

Improved Trainable Calibration Method for Neural Networks.

Gongbo Liang
, Yu Zhang
, Xiaoqin Wang
, Nathan Jacobs

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Original language	Undefined/Unknown
Title of host publication	BMVC
State	Published - 2020

Bibliographical note

DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

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Cite this

@inproceedings{29d223435bcd45678a2f0f1d86b9ecd6,

title = "Improved Trainable Calibration Method for Neural Networks.",

author = "Gongbo Liang and Yu Zhang and Xiaoqin Wang and Nathan Jacobs",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

year = "2020",

language = "Undefined/Unknown",

booktitle = "BMVC",

}

TY - GEN

T1 - Improved Trainable Calibration Method for Neural Networks.

AU - Liang, Gongbo

AU - Zhang, Yu

AU - Wang, Xiaoqin

AU - Jacobs, Nathan

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2020

Y1 - 2020

M3 - Conference contribution

BT - BMVC

ER -