Phase unwrapping is a classical ill-posed problem which aims to recover the true phase
from wrapped phase. In this paper, we introduce a novel Convolutional Neural Network (CNN) …

In real-world applications, the distribution of the data, and our goals, evolve over time. The
prevailing theoretical framework for studying machine learning, namely probably …

Accurate and real-time hand gesture recognition is essential for controlling advanced hand
prostheses. Surface Electromyography (sEMG) signals obtained from the forearm are widely …

Human behavioral monitoring during sleep is essential for various medical applications.
Majority of the contactless human pose estimation algorithms are based on RGB modality, …

Systemic diseases such as diabetes, hypertension, atherosclerosis are among the leading
causes of annual human mortality rate. It is suggested that retinal and conjunctival vascular …

Surface electromyography (sEMG) is a popular bio-signal used for controlling prostheses
and finger gesture recognition mechanisms. Myoelectric prostheses are costly, and most …

Generalization error always improves with more in-distribution data. However, it is an open
question what happens as we add out-of-distribution (OOD) data. Intuitively, if the OOD data …

We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of
domain adaptation. The class is the convex combination of two hypotheses: i) an average …

Learning is a process which can update decision rules, based on past experience, such that
future performance improves. Traditionally, machine learning is often evaluated under the …

We propose and study a data-driven method that can interpolate between a classical and a
modern approach to classification for a class of linear models. The class is the convex …