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IEEE Demo Template for Computer Society ConferencesOfficial
This is a skeleton file demonstrating the use of IEEEtran.cls (requires IEEEtran.cls version 1.8b or later) with an IEEE Computer
Society conference paper.
For other IEEE conferences, please see the IEEE conference paper template, and to find additional IEEE templates please use the tags below.
IEEEtran.cls version: 1.8b
Michael Shell

SBGames 2020 Template
Downloaded from https://www.sbgames.org/sbgames2017/downloads/template-latex-sbgames2017.zip
SBGames

A demonstration of the LaTeX2e class file for SAGE Publications
This paper describes the use of the LaTeX2e sagej.cls class file for setting papers to be submitted to a SAGE Publications journal. The template can be downloaded here.
v1.2, 14 Jan 2017
Alistair Smith and Hendrik Wittkopf

Using ResNet for Pulmonary Nodule Classification
Classifying pulmonary nodule CT images as either benign or malignant, using a trained Residual Neural Network.
Owen Li

ACL 2016 Proceedings Template
This document contains instructions for preparing ACL 2016 submissions and camera-ready manuscripts. The document itself conforms to its own specifications, and is therefore an example of what your manuscript should look like. Papers are required to conform to all the directions reported in this document. By using the provided LaTeX and BibTeX styles (acl2016.sty, acl2016.bst), the required formatting will be enabled by default.
Adi Renduchinala

Template for Submission to IJCAI-19
Template for Submission to IJCAI-19; downloaded from the conference's Author's Kit page.
IJCAI

template_assignment (ECL)
Homework Assignment Article
LaTeX Template
Version 1.3.1 (ECL) (08/08/17)
Victor Zimmermann

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

MicroSympTemplate
Micromouse Symposium article template.
Antonio Valente