Breast cancer is one of the most common invading cancers in women. Analyzing breast
cancer is nontrivial and may lead to disagreements among experts. Although deep learning …

Most capsule network designs rely on traditional matrix multiplication between capsule
layers and computationally expensive routing mechanisms to deal with the capsule …

We present a convolutional neural network design with additional branches after certain
convolutions so that we can extract features with differing effective receptive fields and levels …

Text classification is a central task in Natural Language Processing (NLP) that aims at
categorizing text documents into predefined classes or categories. It requires appropriate …

We propose a novel Global Entropy Pooling (GEP) layer for Convolutional Neural Networks
(CNNs). This is the first approach that uses the Entropy value directly for pooling rather than …

Intrinsic DNA properties including bending play a crucial role in diverse biological systems.
A recent advance in a high-throughput technology called loop-seq makes it possible to …

We show that, for each of five datasets of increasing complexity, certain training samples are
more informative of class membership than others. These samples can be identified a priori …

Deep learning techniques have recently shown promise in the field of anomaly detection,
providing a flexible and effective method of modelling systems in comparison to traditional …

We provide a definition for class density that can be used to measure the aggregate
similarity of the samples within each of the classes in a high-dimensional, unstructured …

In practice, the most important aspect has been to suggest items of interest to users based
on their prior choices. In recent decades, recommendation systems have been highly …