Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Creating pull request for 10.21105.joss.04517 #4130

Closed
wants to merge 3 commits into from
Closed

Creating pull request for 10.21105.joss.04517 #4130

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
7e97467
Creating 10.21105.joss.04517.pdf
editorialbot Apr 17, 2023
06cfaf1
Creating 10.21105.joss.04517.crossref.xml
editorialbot Apr 17, 2023
40ba0be
Creating 10.21105.joss.04517.jats
editorialbot Apr 17, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
288 changes: 288 additions & 0 deletions joss.04517/10.21105.joss.04517.crossref.xml
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,288 @@
<?xml version="1.0" encoding="UTF-8"?>
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1"
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd"
xmlns:rel="http://www.crossref.org/relations.xsd"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
version="5.3.1"
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd">
<head>
<doi_batch_id>20230417T080333-ba9f8acf0e1bcbe008fee256e180a9d292e8ccd5</doi_batch_id>
<timestamp>20230417080333</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>admin@theoj.org</email_address>
</depositor>
<registrant>The Open Journal</registrant>
</head>
<body>
<journal>
<journal_metadata>
<full_title>Journal of Open Source Software</full_title>
<abbrev_title>JOSS</abbrev_title>
<issn media_type="electronic">2475-9066</issn>
<doi_data>
<doi>10.21105/joss</doi>
<resource>https://joss.theoj.org/</resource>
</doi_data>
</journal_metadata>
<journal_issue>
<publication_date media_type="online">
<month>04</month>
<year>2023</year>
</publication_date>
<journal_volume>
<volume>8</volume>
</journal_volume>
<issue>84</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>Efficiently Learning Relative Similarity Embeddings
with Crowdsourcing</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Scott</given_name>
<surname>Sievert</surname>
<ORCID>https://orcid.org/0000-0002-4275-3452</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Robert</given_name>
<surname>Nowak</surname>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Timothy</given_name>
<surname>Rogers</surname>
<ORCID>https://orcid.org/0000-0001-6304-755X</ORCID>
</person_name>
</contributors>
<publication_date>
<month>04</month>
<day>17</day>
<year>2023</year>
</publication_date>
<pages>
<first_page>4517</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.04517</identifier>
</publisher_item>
<ai:program name="AccessIndicators">
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
</ai:program>
<rel:program>
<rel:related_item>
<rel:description>Software archive</rel:description>
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.7832431</rel:inter_work_relation>
</rel:related_item>
<rel:related_item>
<rel:description>GitHub review issue</rel:description>
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/4517</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.04517</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.04517</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.04517.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="erkle">
<article_title>Efficient online relative comparison kernel
learning</article_title>
<author>Heim</author>
<journal_title>Proceedings of the 2015 SIAM international
conference on data mining</journal_title>
<doi>10.1137/1.9781611974010.31</doi>
<cYear>2015</cYear>
<unstructured_citation>Heim, E., Berger, M., Seversky, L.
M., &amp; Hauskrecht, M. (2015). Efficient online relative comparison
kernel learning. Proceedings of the 2015 SIAM International Conference
on Data Mining, 271–279.
https://doi.org/10.1137/1.9781611974010.31</unstructured_citation>
</citation>
<citation key="ste">
<article_title>Stochastic triplet embedding</article_title>
<author>Van Der Maaten</author>
<journal_title>2012 IEEE international workshop on machine
learning for signal processing</journal_title>
<doi>10.1109/MLSP.2012.6349720</doi>
<cYear>2012</cYear>
<unstructured_citation>Van Der Maaten, L., &amp; Weinberger,
K. (2012). Stochastic triplet embedding. 2012 IEEE International
Workshop on Machine Learning for Signal Processing, 1–6.
https://doi.org/10.1109/MLSP.2012.6349720</unstructured_citation>
</citation>
<citation key="ckl">
<article_title>Adaptively learning the crowd
kernel</article_title>
<author>Tamuz</author>
<journal_title>Proceedings of the 28th international
conference on international conference on machine
learning</journal_title>
<isbn>9781450306195</isbn>
<cYear>2011</cYear>
<unstructured_citation>Tamuz, O., Liu, C., Belongie, S.,
Shamir, O., &amp; Kalai, A. T. (2011). Adaptively learning the crowd
kernel. Proceedings of the 28th International Conference on
International Conference on Machine Learning, 673–680.
ISBN: 9781450306195</unstructured_citation>
</citation>
<citation key="next">
<article_title>NEXT: A System for Real-World Development,
Evaluation, and Application of Active Learning</article_title>
<author>Jamieson</author>
<journal_title>Advances in neural information processing
systems</journal_title>
<volume>28</volume>
<cYear>2015</cYear>
<unstructured_citation>Jamieson, K. G., Jain, L., Fernandez,
C., Glattard, N. J., &amp; Nowak, R. (2015). NEXT: A System for
Real-World Development, Evaluation, and Application of Active Learning.
In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, &amp; R. Garnett (Eds.),
Advances in neural information processing systems (Vol. 28). Curran
Associates, Inc.
https://proceedings.neurips.cc/paper/2015/file/89ae0fe22c47d374bc9350ef99e01685-Paper.pdf</unstructured_citation>
</citation>
<citation key="tackl">
<article_title>Active perceptual similarity modeling with
auxiliary information</article_title>
<author>Heim</author>
<journal_title>arXiv preprint
arXiv:1511.02254</journal_title>
<cYear>2015</cYear>
<unstructured_citation>Heim, E., Berger, M., Seversky, L.,
&amp; Hauskrecht, M. (2015). Active perceptual similarity modeling with
auxiliary information. arXiv Preprint arXiv:1511.02254.
https://arxiv.org/pdf/1511.02254.pdf</unstructured_citation>
</citation>
<citation key="smart">
<article_title>SMART: An open source data labeling platform
for supervised learning.</article_title>
<author>Chew</author>
<journal_title>Journal of Machine Learning
Research</journal_title>
<issue>82</issue>
<volume>20</volume>
<cYear>2019</cYear>
<unstructured_citation>Chew, R., Wenger, M., Kery, C.,
Nance, J., Richards, K., Hadley, E., &amp; Baumgartner, P. (2019).
SMART: An open source data labeling platform for supervised learning.
Journal of Machine Learning Research, 20(82), 1–5.
http://jmlr.org/papers/v20/18-859.html</unstructured_citation>
</citation>
<citation key="faceverification">
<article_title>Triplet probabilistic embedding for face
verification and clustering</article_title>
<author>Sankaranarayanan</author>
<journal_title>2016 IEEE 8th international conference on
biometrics theory, applications and systems (BTAS)</journal_title>
<doi>10.1109/BTAS.2016.7791205</doi>
<cYear>2016</cYear>
<unstructured_citation>Sankaranarayanan, S., Alavi, A.,
Castillo, C. D., &amp; Chellappa, R. (2016). Triplet probabilistic
embedding for face verification and clustering. 2016 IEEE 8th
International Conference on Biometrics Theory, Applications and Systems
(BTAS), 1–8.
https://doi.org/10.1109/BTAS.2016.7791205</unstructured_citation>
</citation>
<citation key="vehicles">
<article_title>Vehicle re-identification: An efficient
baseline using triplet embedding</article_title>
<author>Kuma</author>
<journal_title>2019 international joint conference on neural
networks (IJCNN)</journal_title>
<doi>10.1109/IJCNN.2019.8852059</doi>
<cYear>2019</cYear>
<unstructured_citation>Kuma, R., Weill, E., Aghdasi, F.,
&amp; Sriram, P. (2019). Vehicle re-identification: An efficient
baseline using triplet embedding. 2019 International Joint Conference on
Neural Networks (IJCNN), 1–9.
https://doi.org/10.1109/IJCNN.2019.8852059</unstructured_citation>
</citation>
<citation key="chem">
<article_title>Cognitive task analysis for implicit
knowledge about visual representations with similarity learning
methods</article_title>
<author>Mason</author>
<journal_title>Cognitive science</journal_title>
<volume>43</volume>
<doi>10.1111/cogs.12744</doi>
<cYear>2019</cYear>
<unstructured_citation>Mason, B., Rau, M. A., &amp; Nowak,
R. (2019). Cognitive task analysis for implicit knowledge about visual
representations with similarity learning methods. Cognitive Science, 43.
https://doi.org/10.1111/cogs.12744</unstructured_citation>
</citation>
<citation key="agarwal2016multiworld">
<article_title>Making contextual decisions with low
technical debt</article_title>
<author>Agarwal</author>
<journal_title>arXiv preprint
arXiv:1606.03966</journal_title>
<cYear>2016</cYear>
<unstructured_citation>Agarwal, A., Bird, S., Cozowicz, M.,
Hoang, L., Langford, J., Lee, S., Li, J., Melamed, D., Oshri, G., Ribas,
O., &amp; others. (2016). Making contextual decisions with low technical
debt. arXiv Preprint arXiv:1606.03966.
https://arxiv.org/pdf/1606.03966.pdf</unstructured_citation>
</citation>
<citation key="ma2019fast">
<article_title>Fast stochastic ordinal embedding with
variance reduction and adaptive step size</article_title>
<author>Ma</author>
<journal_title>IEEE Transactions on Knowledge and Data
Engineering</journal_title>
<issue>6</issue>
<volume>33</volume>
<doi>10.1109/TKDE.2019.2956700</doi>
<cYear>2021</cYear>
<unstructured_citation>Ma, K., Zeng, J., Xiong, J., Xu, Q.,
Cao, X., Liu, W., &amp; Yao, Y. (2021). Fast stochastic ordinal
embedding with variance reduction and adaptive step size. IEEE
Transactions on Knowledge and Data Engineering, 33(6), 2467–2478.
https://doi.org/10.1109/TKDE.2019.2956700</unstructured_citation>
</citation>
<citation key="soe">
<article_title>Insights into ordinal embedding algorithms: A
systematic evaluation</article_title>
<author>Vankadara</author>
<journal_title>arXiv preprint
arXiv:1912.01666</journal_title>
<cYear>2019</cYear>
<unstructured_citation>Vankadara, L. C., Haghiri, S.,
Lohaus, M., Wahab, F. U., &amp; Luxburg, U. von. (2019). Insights into
ordinal embedding algorithms: A systematic evaluation. arXiv Preprint
arXiv:1912.01666.
https://arxiv.org/abs/1912.01666</unstructured_citation>
</citation>
<citation key="pytorch">
<article_title>PyTorch: An imperative style,
high-performance deep learning library</article_title>
<author>Paszke</author>
<journal_title>Advances in neural information processing
systems</journal_title>
<volume>32</volume>
<cYear>2019</cYear>
<unstructured_citation>Paszke, A., Gross, S., Massa, F.,
Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein,
N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison,
M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., … Chintala, S.
(2019). PyTorch: An imperative style, high-performance deep learning
library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. dAlché-Buc, E.
Fox, &amp; R. Garnett (Eds.), Advances in neural information processing
systems (Vol. 32). Curran Associates, Inc.
https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Loading