8,171 research outputs found

    Entrepreneurship and the Barrier to Exit: How Does an Entrepreneur-Friendly Bankruptcy Law Affect Entrepreneurship Development at a Societal Level?

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    Does an entrepreneur-friendly bankruptcy law encourage more entrepreneurship development at a societal level? How does bankruptcy law affect entrepreneurship development around the world? Drawing on a real options perspective, we argue that if bankrupt entrepreneurs are excessively punished for failure, they may pass potentially high-return but inherently high-risk opportunities. Amassing a longitudinal, cross-country data base from 35 countries spanning ten years, we find that a lenient, entrepreneur-friendly bankruptcy law encourages entrepreneurs to take risks and thus let entrepreneurship prosper. Components of an entrepreneur-friendly bankruptcy law are: (1) the availability of a reorganization bankruptcy option, (2) the time spent on bankruptcy procedure, (3) the cost of bankruptcy procedure, (4) the opportunity to have a fresh start in liquidation bankruptcy, (5) the opportunity to have an automatic stay of assets, (6) the opportunity for managers to remain on the job after filing for bankruptcy, and (7) the protection of creditors at the time of bankruptcy.

    Comparing Sample-wise Learnability Across Deep Neural Network Models

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    Estimating the relative importance of each sample in a training set has important practical and theoretical value, such as in importance sampling or curriculum learning. This kind of focus on individual samples invokes the concept of sample-wise learnability: How easy is it to correctly learn each sample (cf. PAC learnability)? In this paper, we approach the sample-wise learnability problem within a deep learning context. We propose a measure of the learnability of a sample with a given deep neural network (DNN) model. The basic idea is to train the given model on the training set, and for each sample, aggregate the hits and misses over the entire training epochs. Our experiments show that the sample-wise learnability measure collected this way is highly linearly correlated across different DNN models (ResNet-20, VGG-16, and MobileNet), suggesting that such a measure can provide deep general insights on the data's properties. We expect our method to help develop better curricula for training, and help us better understand the data itself.Comment: Accepted to AAAI 2019 Student Abstrac

    Exchange rate regimes and international business cycle transmission revisited

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    노트 : A paper prepared for the conference on 'Korea and the World Economy', 21-22 July 2002, Seoul, South Korea
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