The large model could then implement a simple learning algorithm to train this smaller, linear model to complete a new task, using only information already contained within the larger model.

In this chapter, we propose a log-linear model for the biases observed when analyzing model communities data. Our model expands the recent work from McLaren, Willis and Callahan (MWC) [eLife, 8:e46923 ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...

We consider marked empirical processes indexed by a randomly projected functional covariate to construct goodness-of-fit tests for the functional linear model with scalar response. The test statistics ...

Recent literature contains several expositions of the log-linear modeling (LLM) capability of analyzing multiway contingency tables. This method has been proposed as a way of overcoming the ...

In traditional models like linear regression and ANOVA, assumptions such as linearity, independence of errors, homoscedasticity, and normality of residuals are foundational.

In a thermally linear system, superposition may be applied to predict the transient response of the system to step changes in input power. This article is the second in a two-part series.