The prevalence of relational data in several real-world applications, e.g., social network analysis, recommendation systems, and neurological modeling has led to crucial advances in machine learning techniques for graph-structured data. This encompasses a wide-range of formulations to mine and gather insights from complex network datasets — node...

Background The implementation of deep learning in a wide range of applications has resulted in an ever-increasing complexity of neural network architecture. These network architectures require reconfiguration to support specific applications, particularly in the field of computer vision where an image may contain a variety of objects needing to be...

Background In automatic speech processing systems, speaker diarization is a crucial front-end component for separating speech segments by speaker without a priori knowledge about speaker identities. The first phase of many state-of-the-art diarization techniques involves the conversion of original speech data into representative i-vectors, which are...