14 citations · 21 across the 8 of their papers we have counts for
10 papers
What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability
John Kirchenbauer, Jacob Oaks, Eric Heim
Classifier calibration has received recent attention from the machine learning community due both to its practical utility in facilitating decision making, as well as the observati…
Factor Analysis on Citation, Using a Combined Latent and Logistic Regression Model
Namjoon Suh, Xiaoming Huo, Eric Heim +1
We propose a combined model, which integrates the latent factor model and the logistic regression model, for the citation network. It is noticed that neither a latent factor model…
Constrained Generative Adversarial Networks for Interactive Image Generation
Eric Heim
Generative Adversarial Networks (GANs) have received a great deal of attention due in part to recent success in generating original, high-quality samples from visual domains. Howev…
Exploiting Class Learnability in Noisy Data
Matthew Klawonn, Eric Heim, James Hendler
In many domains, collecting sufficient labeled training data for supervised machine learning requires easily accessible but noisy sources, such as crowdsourcing services or tagged…
Generating Triples with Adversarial Networks for Scene Graph Construction
Matthew Klawonn, Eric Heim
Driven by successes in deep learning, computer vision research has begun to move beyond object detection and image classification to more sophisticated tasks like image captioning…
Clickstream analysis for crowd-based object segmentation with confidence
Eric Heim, Alexander Seitel, Jonas Andrulis +4
With the rapidly increasing interest in machine learning based solutions for automatic image annotation, the availability of reference annotations for algorithm training is one of…