Regularizing Neural Networks via Adversarial Model Perturbation
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Effective regularization schemes are highly desired in deep learning for alleviating overfitting and improving generalization.
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baposter[landscape, a0paper, fontscale=0.3]- Compiles with
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- 18 files: 1 .tex, 16 images, 1 class or style file
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Regularizing Neural Networks via adversarial Model Perturbation/baposter.cls
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timescalcurlgraphicxamsthmamsmathamssymbrelsizemultirowmulticolbooktabsmicrotypefontencaeenumitemcolortblxcolortcolorboxalgorithm2emintedxkeyvaltikzpgfifthengeometrypgfpages
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\usepackage{multicol}
\usepackage{booktabs}
\usepackage{microtype}
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\usepackage[T1]{fontenc}
\usepackage{ae}
\usepackage{enumitem}
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\usepackage[vlined]{algorithm2e}
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% \renewcommand{\rmdefault}{ptm} % Arial
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It uses the baposter class (options: landscape, a0paper, fontscale=0.3), builds with pdfLaTeX, and loads 26 packages, including times, calc, url, graphicx, amsthm, amsmath, amssymb and relsize. It ships its own Regularizing Neural Networks via adversarial Model Perturbation/baposter.cls, which is part of the project when you open it.
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