Adapting to the addressee is crucial for successful explanations, yet poses significant challenges for dialog systems. We adopted the approach of treating explanation generation as a non-stationary ...

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...

When a natural disaster strikes, first responder managers face a flood of data — from drones, sensors, cameras, satellites, police, firefighters, and citizens — that must be sorted, secured, and ...

ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

Abstract: One of the key challenges in systems biology is to derive gene regulatory networks (GRNs) from complex high-dimensional sparse data. Bayesian networks (BNs) and dynamic Bayesian networks ...

Anomaly response in aerospace systems increasingly relies on multi-model analysis in digital twins to replicate the system’s behaviors and inform decisions. However, computer model calibration methods ...