6 citations · 6 across the 5 of their papers we have counts for
5 papers
How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance
Jerry Y. Huang, Justin Lin, Sheel Shah +2
In generative modeling, we often wish to produce samples that maximize a user-specified reward such as aesthetic quality or alignment with human preferences, a problem known as \te…
A comparative analysis of machine learning models in SHAP analysis
Justin Lin, Julia Fukuyama
In this growing age of data and technology, large black-box models are becoming the norm due to their ability to handle vast amounts of data and learn incredibly complex data patte…
SHAP-Based Supervised Clustering for Sample Classification and the Generalized Waterfall Plot
Justin Lin, Julia Fukuyama
In this growing age of data and technology, large black-box models are becoming the norm due to their ability to handle vast amounts of data and learn incredibly complex input-outp…
DRtool: An Interactive Tool for Analyzing High-Dimensional Clusterings
Justin Lin, Julia Fukuyama
When faced with new data, we often conduct a cluster analysis to obtain a better understanding of the data's structure and the archetypical samples present in the data. However, th…
Calibrating dimension reduction hyperparameters in the presence of noise
Justin Lin, Julia Fukuyama
The goal of dimension reduction tools is to construct a low-dimensional representation of high-dimensional data. These tools are employed for a variety of reasons such as noise red…