58 citations · 140 across the 15 of their papers we have counts for
5 papers · 1 filter
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit
Stefan Kolek, Aditya Chattopadhyay, Kwan Ho Ryan Chan +3
Information Pursuit (IP) is an explainable prediction algorithm that greedily selects a sequence of interpretable queries about the data in order of information gain, updating its…
Contextual Knowledge Pursuit for Faithful Visual Synthesis
Jinqi Luo, Kwan Ho Ryan Chan, Dimitris Dimos +1
Modern text-to-vision generative models often hallucinate when the prompt describing the scene to be generated is underspecified. In large language models (LLMs), a prevalent strat…
Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions
Kwan Ho Ryan Chan, Aditya Chattopadhyay, Benjamin David Haeffele +1
Variational Information Pursuit (V-IP) is a framework for making interpretable predictions by design by sequentially selecting a short chain of task-relevant, user-defined and inte…
Variational Information Pursuit for Interpretable Predictions
Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin D. Haeffele +2
There is a growing interest in the machine learning community in developing predictive algorithms that are "interpretable by design". Towards this end, recent work proposes to make…
Unsupervised Manifold Linearizing and Clustering
Tianjiao Ding, Shengbang Tong, Kwan Ho Ryan Chan +3
We consider the problem of simultaneously clustering and learning a linear representation of data lying close to a union of low-dimensional manifolds, a fundamental task in machine…