We consider the problem of modeling the mean function in regression. Often there is enough knowledge to model some components of the mean function parametrically. But for other vague and/or nuisance ...

Partially linear models provide a useful class of tools for modeling complex data by naturally incorporating a combination of linear and nonlinear effects within one framework. One key question in ...

We leverage recent advancements in machine learning to develop an integrated method to solve globally and estimate models featuring agent heterogeneity, nonlinear constraints, and aggregate ...

A standard digital camera used in a car for stuff like emergency braking has a perceptual latency of a hair above 20 milliseconds. That’s just the time needed for a camera to transform the photons ...

Recognizing the nonlinear behavior of the 12AX7 vacuum tube, used in audio and old op-amp applications, Dr. Dyer compares various models for fit to real-world device data. Given appropriate models for ...

In today's machine learning field, deep neural network models are becoming increasingly large and complex, posing significant challenges to traditional electronic computing hardware. To address this ...

Highly reduced order models with nonlinear contact – extremely efficient computation. Modeled as linear components interconnected with highly reduced-order components. 1-linear reduced model, ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy ...