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IEEE Conference Template
IEEE Conference Template
This demo file is intended to serve as a "starter file'' for IEEE conference papers produced under LaTeX. This is one of a number of templates using the IEEE style that are available on Overleaf to help you get started - use the tags below to find more.
IEEE
Conservative Wasserstein Training for Pose Estimation
Conservative Wasserstein Training for Pose Estimation
Paper presented at ICCV 2019. This paper targets the task with discrete and periodic class labels (e.g., pose/orientation estimation) in the context of deep learning. The commonly used cross-entropy or regression loss is not well matched to this problem as they ignore the periodic nature of the labels and the class similarity, or assume labels are continuous value. We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining (i.e., using arc length of a circle) or adaptively learning the ground metric. We extend the ground metric as a linear, convex or concave increasing function w.r.t. arc length from an optimization perspective. We also propose to construct the conservative target labels which model the inlier and outlier noises using a wrapped unimodal-uniform mixture distribution. Unlike the one-hot setting, the conservative label makes the computation of Wasserstein distance more challenging. We systematically conclude the practical closed-form solution of Wasserstein distance for pose data with either one-hot or conservative target label. We evaluate our method on head, body, vehicle and 3D object pose benchmarks with exhaustive ablation studies. The Wasserstein loss obtaining superior performance over the current methods, especially using convex mapping function for ground metric, conservative label, and closed-form solution.
Xiaofeng Liu, Yang Zou, Tong Che, Peng Ding, Ping Jia, Jane You, B.V.K. Vijaya Kumar
ESANN V2 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
ESANN V2 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
This is a unofficial template to ESANN (European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning) submissions, the project was built using the author guidelines published in: https://www.esann.org/node/5 (accessed in november-2019) About ESANN: This event builds upon a very successful series of conference organized each year since 1993. ESANN has become a major scientific events in the machine learning, computational intelligence and artificial neural networks fields over the years.
Deuslirio Junior
AACL-IJCNLP 2020 Proceedings Template
AACL-IJCNLP 2020 Proceedings Template
Used for AACL-IJCNLP manuscript submissions. Note from Overleaf: SyncTeX will not work correctly with this template (as well as other templates based on similar underlying code, eg CVPR, EMNLP, etc) when the line numbers are active. To make SyncTeX function while authoring your manuscript, either on Overleaf or in your own LaTeX installation, the line numbers have to be turned off by uncommenting \aclfinalcopy.
AACL-IJCNLP 2020 PC Chairs
Language Resources and Evaluation Conference 2020 LaTeX template
Language Resources and Evaluation Conference 2020 LaTeX template
12th Edition of the Language Resources and Evaluation Conference LaTeX template. Source: https://lrec2020.lrec-conf.org/en/submission2020/authors-kit/.
LREC 2020 Organizers
Modelo INATEL INCITEL
Modelo INATEL INCITEL
Modelo de escrita de artigo científico para o INCITEL - Congresso de Iniciação Científica do Inatel.
Phyllipe Lima
Symposium on Data Science and Statistics (SDSS) 2020 template for contributed papers
Symposium on Data Science and Statistics (SDSS) 2020 template for contributed papers
We have based this SDSS template almost entirely on the template for the annual conference of the Association for Computational Linguistics (ACL). This template is for contributed paper submissions—Symposium on Data Science and Statistics (SDSS) 2020.
Dave Hunter and Donna LaLonde
Preparation of Papers for IEEE Sponsored Conferences and Symposia
Preparation of Papers for IEEE Sponsored Conferences and Symposia
This is a LaTeX template for preparing documents for IEEE Sponsored Conferences and Symposia. It was submitted by an author writing for the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’14). The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
Huibert Kwakernaak and Pradeep Misra
ACM Conference Proceedings "Master" Template
ACM Conference Proceedings "Master" Template
ACM has transitioned to a new authoring template. This new TeX template consolidates the previous eight individual ACM journal and proceedings templates. The templates are updated to the latest software versions, developed to enable accessibility features, and they use a new font set. The new LaTeX package incorporates updated versions of the following ACM templates: ACM Journals: ACM Small, ACM Large, ACM and TOG (also for SIGGRAPH authors publishing in TOG) ACM proceedings templates: ACM Standard, SIGCHI, SIGCHI abstracts, and SIGPLAN All journals use acmsmall with the following exceptions: acmlarge - Large single column format, used for IMWUT, JOCCH, PACMPL, POMACS, TAP, PACMHCI acmtog - Large double column format, used for TOG Note: Most proceedings authors will use the "sigconf" proceedings template. If you are unsure which template variant to use, please request clarification from your event or publication contact. Before using the 2017 ACM consolidated proceedings template, we strongly suggest that you read the TeX User Guide. Authors who plan to use their own packages should read the longer Implementation Guide. More detailed Instructions for Authors are found at http://www.acm.org/publications/authors/information-for-authors. It is important to provide the proper indexing information from the ACM Computing Classification System (CCS). Accurate semantic tagging provides a reader with quick content reference; facilitates the DL search for related literature; enables several DL topic functions such as aggregated SIG and journal coverage areas; and helps ACM promote your work in other online resources. Once your conference proceedings submission is ready, you can use the “Submit to ACM” button at the top of the Overleaf editor bar to quickly download your files. Please then refer to the submission guidelines in the relevant call-for-papers or on the event website for information on where to submit. For support on using these templates, or on LaTeX in general, please contact the Overleaf team -- we're happy to help.
Association for Computing Machinery (ACM)

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