Ohio State University

About: Ohio State University is a education organization based out in Columbus, Ohio, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 102421 authors who have published 222715 publications receiving 8373403 citations. The organization is also known as: Ohio State & The Ohio State University.

Reconciling modern machine-learning practice and the classical bias-variance trade-off.

Abstract: Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias-variance trade-off, appears to be at odds with the observed behavior of methods used in modern machine-learning practice. The bias-variance trade-off implies that a model should balance underfitting and overfitting: Rich enough to express underlying structure in data and simple enough to avoid fitting spurious patterns. However, in modern practice, very rich models such as neural networks are trained to exactly fit (i.e., interpolate) the data. Classically, such models would be considered overfitted, and yet they often obtain high accuracy on test data. This apparent contradiction has raised questions about the mathematical foundations of machine learning and their relevance to practitioners. In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This "double-descent" curve subsumes the textbook U-shaped bias-variance trade-off curve by showing how increasing model capacity beyond the point of interpolation results in improved performance. We provide evidence for the existence and ubiquity of double descent for a wide spectrum of models and datasets, and we posit a mechanism for its emergence. This connection between the performance and the structure of machine-learning models delineates the limits of classical analyses and has implications for both the theory and the practice of machine learning.

Beyond the Qualitative Interview: Data Preparation and Transcription

Abstract: The increased use of qualitative research, especially its application in multisite studies, requires robust data collection techniques and the documentation of research procedures. The inappropriate or inadequate preparation of transcripts from audio or digital recordings can delay or negatively affect the analysis process. Although no universal transcription format is adequate for all types of qualitative data collection approaches, settings, or theoretical frameworks, there are some practical considerations that can help researchers systematically organize and analyze textual data.