104 citations · 209 across the 10 of their papers we have counts for
7 papers · 1 filter
Multimodal Generative Models for Compositional Representation Learning
Mike Wu, Noah Goodman
As deep neural networks become more adept at traditional tasks, many of the most exciting new challenges concern multimodality---observations that combine diverse types, such as im…
Gradient Boosting Machine: A Survey
Zhiyuan He, Danchen Lin, Thomas Lau +1
In this survey, we discuss several different types of gradient boosting algorithms and illustrate their mathematical frameworks in detail: 1. introduction of gradient boosting lead…
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization
Mike Wu, Sonali Parbhoo, Michael C. Hughes +2
Deep models have advanced prediction in many domains, but their lack of interpretability remains a key barrier to the adoption in many real world applications. There exists a large…
Regional Tree Regularization for Interpretability in Black Box Models
Mike Wu, Sonali Parbhoo, Michael Hughes +5
The lack of interpretability remains a barrier to the adoption of deep neural networks. Recently, tree regularization has been proposed to encourage deep neural networks to resembl…
Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems
Ali Malik, Mike Wu, Vrinda Vasavada +5
Access to high-quality education at scale is limited by the difficulty of providing student feedback on open-ended assignments in structured domains like computer programming, grap…
Pragmatic inference and visual abstraction enable contextual flexibility during visual communication
Judith Fan, Robert Hawkins, Mike Wu +1
Visual modes of communication are ubiquitous in modern life --- from maps to data plots to political cartoons. Here we investigate drawing, the most basic form of visual communicat…