Abstract Learning discriminative representations with deep neural networks often relies on massive labeled data, which is expensive and difficult to obtain in many real scenarios. As an alternative, self-supervised learning that leverages input itself as supervision strongly preferred for its soaring performance visual representation learning. This paper introduces a contrastive framework gener...

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various unknown characteristics. Fully synthetic usually consists of regular instances clear characteristics thus allows for a more meaningful evaluation methods in principle. Nonetheless, there have only been few attempts to include benchmarks detection. This might be...

Hospital overcrowding is a major problem for healthcare systems around the globe. In order to better estimate future demands and adequate resources coping with such demands, statistical computerised modelling can be applied. This then allow administrators decision makers quantify impacts of various “what-if” scenarios on hospital performance measures. paper investigates application Discrete Eve...

Synthetic datasets are gradually emerging as solutions for data sharing. Multiple synthetic generators have been introduced in the last decade fueled by advancement machine learning and increased demand fast inclusive sharing, yet their utility is not well understood. Prior research tried to compare of using different evaluation metrics. These metrics found generate conflicting conclusions maki...