Comorbid anxiety predicts lower odds of depression improvement during smartphone-delivered psychotherapy.

Comorbid anxiety disorders are common among patients with major depressive disorder (MDD), and numerous studies have identified an association between comorbid anxiety and resistance to pharmacological depression treatment. However, the impact of anxiety on the effectiveness of non-pharmacological interventions for MDD is not as well understood. In this study, we applied machine learning techniques to predict treatment responses in a large-scale clinical trial ( = 493) of individuals with MDD, who were recruited online and randomly assigned to one of three smartphone-based interventions.

Learning to Learn: How to Continuously Teach Humans and Machines.

Curriculum design is a fundamental component of education. For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication. These and other concepts must be mastered before our first algebra lesson, which also reinforces our addition and multiplication skills.

Tuned Compositional Feature Replays for Efficient Stream Learning.

Our brains extract durable, generalizable knowledge from transient experiences of the world. Artificial neural networks come nowhere close to this ability. When tasked with learning to classify objects by training on nonrepeating video frames in temporal order (online stream learning), models that learn well from shuffled datasets catastrophically forget old knowledge upon learning new stimuli.