One way of modelling a given process is by fitting a machine learning model to the data it produces. Ideally, we would like the model to be flexible enough to capture all predictable patterns. At the same time, we want it to be interpretable so that we can learn about the process by ...
False rumors (often termed “fake news”) on social media pose a significant threat to modern societies. However, potential reasons for the widespread diffusion of false rumors have been underexplored. In this work, we analyze whether sentiment words, as well as different emotional words, in soci...
Digital learning games are thought to support learning by increasing enjoyment and promoting deeper engagement with the content, but few studies have empirically tested hypothesized pathways between digital learning games and learning outcomes. Decimal P
Conclusions and Relevance Gunshot violence follows an epidemic-like process of social contagion that is transmitted through networks of people by social interactions. Violence prevention efforts that account for social contagion, in addition to demographics, have the potential to prevent more shootings than...
Throughout childhood, adolescence, and young adulthood, the brain undergoes major structural changes that facilitate the emergence of complex behavior and cognition1,2. Mental disorders often surface during this period3and are increasingly understood as resulting from disruptions to normative brain maturatio...
Model calibration is the process of obtaining a trained model and applying a post-processing procedure to enhance its probability estimation. Let input images 𝑋∈𝑥 and class labels 𝑌∈𝒴={1,…,𝑘} be random variables following the joint ground-truth distribution 𝜋(𝑋,𝑌)=𝜋(...
the lack of interpretability [4]. The model is interpretable because it is small and basic enough to be completely comprehended. Ideally, the user should understand the learning process well enough to realize how it forms the decision limits from the training data and why the model has these ...