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<div class="WordSection1">
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">PDF CFP available from: <a href="https://github.com/P-N-Suganthan/CFP">
https://github.com/P-N-Suganthan/CFP</a><o:p></o:p></p>
<p style="margin-bottom:0cm;background:white"><span lang="EN-US" style="font-size:14.0pt;font-family:"Calibri",sans-serif;color:#222222">To submit to this special session, please use this link:<o:p></o:p></span></p>
<p style="margin-bottom:0cm;background:white"><span lang="EN-US" style="color:black"><a href="https://edas.info/newPaper.php?c=30081&track=116093"><span style="font-family:"Courier New"">https://edas.info/newPaper.php?c=30081&track=116093</span></a></span><span lang="EN-US" style="font-family:"Courier New";color:#222222">
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:black;background:white"><o:p> </o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><a href="https://www.bing.com/ck/a?!&&p=6a4d92a9b849f8eeJmltdHM9MTY2ODI5NzYwMCZpZ3VpZD0yZTg3NWZlZS01YzA0LTYxNDItMzJjMy00ZjRmNWRiNjYwODgmaW5zaWQ9NTIwMA&ptn=3&hsh=3&fclid=2e875fee-5c04-6142-32c3-4f4f5db66088&psq=IJCNN&u=a1aHR0cHM6Ly8yMDIzLmlqY25uLm9yZy8&ntb=1" target="_blank"><b><span style="font-size:13.0pt;line-height:105%;font-family:"Arial",sans-serif;color:windowtext;text-decoration:none">International
Joint Conference on Neural Networks 2023</span></b></a><b><span style="font-size:13.0pt;line-height:105%;font-family:"Arial",sans-serif">
</span></b><b><span style="font-size:13.0pt;line-height:105%;font-family:"Arial",sans-serif;mso-fareast-language:ZH-CN"><o:p></o:p></span></b></p>
<p class="MsoNormal"><b><span style="font-size:13.0pt;line-height:105%;font-family:"Arial",sans-serif">Call for Papers for Special Session on</span></b><b><span style="font-size:13.0pt;line-height:105%;font-family:"Arial",sans-serif;mso-fareast-language:ZH-CN"><o:p></o:p></span></b></p>
<p class="MsoNormal"><b><span style="font-size:16.0pt;line-height:105%;font-family:"Arial",sans-serif;color:black;background:white">Randomization-Based Deep and Shallow Learning Algorithms
</span></b><b><span style="font-size:16.0pt;line-height:105%;font-family:"Arial",sans-serif;color:black;background:white"><o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#222222">Randomization-based learning algorithms have received considerable attention from academics, researchers, and domain workers because randomization-based neural networks can be trained
by non-iterative approaches possessing closed-form solutions. Those methods are generally computationally faster than iterative solutions and less sensitive to parameter settings. Even though randomization-based non-iterative methods have attracted much attention
in recent years, their deep structures have not been sufficiently developed nor benchmarked. This special session aims to bridge this gap.</span><span style="font-size:10.5pt;line-height:105%;font-family:"Arial",sans-serif;color:#222222"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#222222">The first target of this special session is to present the recent advances in randomization- based learning methods. Randomization-based neural networks usually offer non-iterative
closed-form solutions. Secondly, the focus is on promoting the concepts of non-iterative optimization with respect to counterparts, such as gradient-based methods and derivative-free iterative optimization techniques. Besides the dissemination of the latest
research results on randomization-based and/or non-iterative algorithms, it is also expected that this special session will cover some practical applications, present some new ideas and identify directions for future studies.
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#222222">Original contributions as well as comparative studies among randomization-based and non-randomized-based methods are welcome with unbiased literature review and comparative studies.
Original contributions having biomedical applications with or without randomization algorithms are also welcome. Typical deep/shallow paradigms include (but not limited to) random vector functional link (RVFL / ensemble deep RVFL), randomized recurrent networks
(RRN), kernel ridge regression (KRR) with randomization, extreme learning machines (ELM), random forests (RF), stochastic configuration network (SCN), broad learning system (BLS), convolution neural networks (CNN) with randomization, and so on.
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:black;background:white;mso-fareast-language:ZH-CN"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:"Arial",sans-serif;color:black;background:white">Topics:<o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:black;background:white">The topics</span><span style="font-family:"Arial",sans-serif;color:#222222"> of the special session include (with randomization-based methods), but are not limited
to:</span><span style="font-family:"Arial",sans-serif;color:black;background:white"><o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Randomized convolutional neural networks<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Randomized internal representation learning<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Regression, classification, and time series analysis by randomization-based methods<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Kernel methods such as kernel ridge regression, kernel adaptive filters, etc. with randomization<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Feedforward, recurrent, multilayer, deep and other structures with randomization<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Ensemble deep learning with randomization such as the edRVFL<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Moore-Penrose pseudo inverse, SVD and other solution procedures.
<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Gaussian process regression<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Randomization-based methods using novel fuzzy approaches<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Randomization-based methods for large-scale problems with and without kernels<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Theoretical analysis of randomization-based methods<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Comparative studies with competing methods without randomization<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Deep randomized convolutional neural networks<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Random/Rotation forests, oblique random forest, and XGBoost based methods<o:p></o:p></span></p>
<p class="MsoListParagraph" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:21.0pt;text-align:justify;text-indent:-21.0pt;line-height:normal;mso-list:l1 level1 lfo1">
<![if !supportLists]><span style="font-family:Wingdings;color:#222222"><span style="mso-list:Ignore">l<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:"Arial",sans-serif;color:#222222">Applications of randomized methods in areas such as biomedicine, finance, economics, signal processing, big data and all other relevant areas<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;line-height:105%;font-family:"Arial",sans-serif;color:black;background:white;mso-fareast-language:ZH-CN"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><u><span style="font-size:16.0pt;line-height:105%;font-family:"Arial",sans-serif">Organizers<o:p></o:p></span></u></b></p>
<p class="MsoNormal"><a name="_Hlk123996289"><span style="font-family:"Arial",sans-serif;color:#222222">P. N. Suganthan, Qatar
</span></a><span style="mso-bookmark:_Hlk123996289"><span style="font-family:"Arial",sans-serif;color:#222222">University.
</span></span><a href="mailto:p.n.suganthan@qu.edu.qa"><span style="mso-bookmark:_Hlk123996289"><span style="font-family:"Arial",sans-serif">p.n.suganthan@qu.edu.qa</span></span><span style="mso-bookmark:_Hlk123996289"></span></a><span style="mso-bookmark:_Hlk123996289"><span style="font-family:"Arial",sans-serif;color:#222222">
</span></span><span style="mso-bookmark:_Hlk123996289"><span style="font-size:10.5pt;line-height:105%;color:#222222"><o:p></o:p></span></span></p>
<span style="mso-bookmark:_Hlk123996289"></span>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#222222">M. Tanveer, Indian Institute of Technology Indore, India.
</span><a href="mailto:mtanveer@iiti.ac.in"><span style="font-family:"Arial",sans-serif">mtanveer@iiti.ac.in</span></a><span class="MsoHyperlink"><span style="font-family:"Arial",sans-serif"><o:p></o:p></span></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#222222">Yudong Zhang, University of Leicester, UK.
</span><span style="font-family:"Arial",sans-serif"><a href="mailto:yudong.zhang@le.ac.uk">yudong.zhang@le.ac.uk</a></span><span style="color:#222222"><o:p></o:p></span></p>
<p class="MsoNormal"><span class="MsoHyperlink"><span style="font-size:12.0pt;line-height:105%"><o:p><span style="text-decoration:none"> </span></o:p></span></span></p>
<p class="MsoNormal"><b><u><span style="font-size:16.0pt;line-height:105%;font-family:"Arial",sans-serif">Important Dates</span></u></b><b><span style="font-size:16.0pt;line-height:105%;mso-fareast-language:ZH-CN"><o:p></o:p></span></b></p>
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<li class="MsoListParagraph" style="color:#222222;margin-bottom:0cm;margin-left:0cm;text-align:justify;line-height:normal;mso-list:l0 level1 lfo2">
<span style="font-size:12.0pt;font-family:"Arial",sans-serif">Jan 31, 2023– First Paper submission deadline (Extension may be offered)<o:p></o:p></span></li><li class="MsoListParagraph" style="color:#222222;margin-bottom:0cm;margin-left:0cm;text-align:justify;line-height:normal;mso-list:l0 level1 lfo2">
<span style="font-size:12.0pt;font-family:"Arial",sans-serif">March 31, 2023 – Paper acceptance notification<o:p></o:p></span></li><li class="MsoListParagraph" style="color:#222222;margin-bottom:0cm;margin-left:0cm;text-align:justify;line-height:normal;mso-list:l0 level1 lfo2">
<span style="font-size:12.0pt;font-family:"Arial",sans-serif">June 18-23, 2023– Gold Coast Convention Centre, Queensland, Australia<o:p></o:p></span></li></ul>
<p class="MsoNormal"><span style="font-size:12.0pt;line-height:105%;font-family:"Arial",sans-serif;color:#222222"><o:p> </o:p></span></p>
<p class="MsoNormal"><a name="_Hlk123996661"><b><u><span style="font-size:16.0pt;line-height:105%;font-family:"Arial",sans-serif;color:black;background:white">Paper Submission</span></u></b></a><span style="mso-bookmark:_Hlk123996661"><b><u><span style="font-size:16.0pt;line-height:105%;font-family:"Arial",sans-serif;color:black;background:white;mso-fareast-language:ZH-CN"><o:p></o:p></span></u></b></span></p>
<p style="text-align:justify;background:white"><span style="mso-bookmark:_Hlk123996661"><span lang="EN-US" style="font-size:10.5pt;font-family:"Arial",sans-serif;color:#222222">Papers submitted to this Special Session are reviewed according to the same rules
as the submissions to the regular sessions of IJCNN 2023. Authors who submit papers to this session are invited to mention it in the form during the submission. Submissions to regular and special sessions follow identical format, instructions, deadlines, and
review procedures. Please, for further information and news refer to the IJCNN website:
</span></span><a href="https://2023.ijcnn.org/"><span style="mso-bookmark:_Hlk123996661"><span lang="EN-US" style="font-size:10.5pt;font-family:"Arial",sans-serif">https://2023.ijcnn.org/</span></span><span style="mso-bookmark:_Hlk123996661"></span></a><span style="mso-bookmark:_Hlk123996661"><span lang="EN-US" style="font-size:10.5pt;font-family:"Arial",sans-serif;color:#222222">
</span><span lang="EN-US"><o:p></o:p></span></span></p>
<p style="margin-bottom:0cm;background:white"><span style="mso-bookmark:_Hlk123996661"><span lang="EN-US" style="font-size:14.0pt;font-family:"Calibri",sans-serif;color:#222222">To submit to this special session, please use this link:<o:p></o:p></span></span></p>
<p style="margin-bottom:0cm;background:white"><span style="mso-bookmark:_Hlk123996661"></span><a href="https://edas.info/newPaper.php?c=30081&track=116093"><span style="mso-bookmark:_Hlk123996661"><span lang="EN-US" style="font-family:"Courier New"">https://edas.info/newPaper.php?c=30081&track=116093</span></span><span style="mso-bookmark:_Hlk123996661"></span></a><span style="mso-bookmark:_Hlk123996661"><span lang="EN-US" style="font-family:"Courier New";color:#222222">
<o:p></o:p></span></span></p>
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<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:black;background:white"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:black;background:white"><o:p> </o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
</div>
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