Deep learning has allowed a paradigm shift in pattern recognition, from using hand-crafted features together with statistical classifiers to using general-purpose learning procedures for learning data-driven representations, features, and classifiers together. The application of this new paradigm has been particularly successful in computer vision, in which the development of deep learning meth...

This paper provides a new perspective on the default prediction problem using deep learning algorithms. Via the advantages of deep learning, the representable factors of input data will no longer need to be explicitly extracted, but can be implicitly learned by the deep learning algorithms. We consider the stock returns of both default and solvent companies as input signals and adopt one of the...

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural netwo...

Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case of deep linear neural networks. Despite the linearity of their input-output ma...

Deep learning, a rebranding of deep neural network research works, has achieved remarkable success in recent years. With multiple hidden layers, deep learning models aim at computing hierarchical features or representations of the observational data. Meanwhile, due to its severe disadvantages in data consumption, computational resources, parameter tuning e‚orts and the lack of result explainabi...

Background: Should we input known genome sequence features or input sequence itself in deep learning framework? As deep learning more popular in various applications, researchers often come to question whether to generate features or use raw sequences for deep learning. To answer this question, we study the prediction accuracy of precursor miRNA prediction of feature-based deep belief network a...

The modern incarnation of neural networks, now popularly known as Deep Learning (DL), accomplished record-breaking success in processing diverse kinds of signals vision, audio, and text. In parallel, strong interest has ensued towards constructing a theory of DL. This paper opens up a group theory based approach, towards a theoretical understanding of DL, in particular the unsupervised variant....