This jupyter notebook tutorial is meant to be a general introduction to machine and deep learning. We use seismic time series data from i) real earthquakes and ii) nuisance signals to train a suite of ...

Binary cross-entropy (BCE) is the default loss function for binary classification—but it breaks down badly on imbalanced datasets. The reason is subtle but important: BCE weighs mistakes from both ...

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

Biological sex is usually described in simple binary terms: male or female. This works well for germ cells (sperm versus eggs), but for other body organs it is of little help. A new study by the Max ...

Abstract: Robotics research encompasses a wide range of technical challenges and interdisciplinary approaches. This study introduces a dual-paradigm classification framework for organizing the stated ...

ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...

Abstract: We consider a human-automation team jointly solving binary classification tasks over multiple time stages. At each stage, the automation observes the data for a batch of classification tasks ...

The advancement of large language models (LLMs) has significantly influenced interactive technologies, presenting both benefits and challenges. One prominent issue arising from these models is their ...