Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

It is currently challenging to analyze single-cell data consisting of many cells and samples,
and to address variations arising from batch effects and different sample preparations. For …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

The availability of massive data and computing power allowing for effective data driven
neural approaches is having a major impact on machine learning and information retrieval …

In the age of big data, time series are being generated in massive amounts. In the energy
field, smart grids are enabling a unprecedented data acquisition with the integration of …

Despite their great success in practical applications, there is still a lack of theoretical and
systematic methods to analyze deep neural networks. In this paper, we illustrate an …

RMSProp and ADAM continue to be extremely popular algorithms for training neural nets
but their theoretical convergence properties have remained unclear. Further, recent work …

Abstract\emph {Contrastive learning}(CL) pretrains models in a pairwise manner, where
given a data point, other data points are all regarded as dissimilar, including some that …

The amount of time series data generated in Healthcare is growing very fast and so is the
need for methods that can analyse these data, detect anomalies and provide meaningful …