Skip to content

Commit

Permalink
Merge pull request #4136 from openjournals/joss.04517
Browse files Browse the repository at this point in the history
Merging automatically
  • Loading branch information
editorialbot authored Apr 17, 2023
2 parents 5bd5667 + 9a52653 commit 4871971
Show file tree
Hide file tree
Showing 3 changed files with 852 additions and 0 deletions.
288 changes: 288 additions & 0 deletions joss.04517/10.21105.joss.04517.crossref.xml
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>20230417T155741-ba9f8acf0e1bcbe008fee256e180a9d292e8ccd5</doi_batch_id>
<timestamp>20230417155741</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>[email protected]</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:/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

0 comments on commit 4871971

Please sign in to comment.