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Found 8 papers, 4 papers with code
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit
To solve the optimization problem, we propose a new query dictionary learning algorithm inspired by classical sparse dictionary learning.
Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions
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 interpretable queries about the data that are most informative for the task.
Variational Information Pursuit for Interpretable Predictions
We then demonstrate that the IP strategy is the optimal solution to this problem.
Interpretable by Design: Learning Predictors by Composing Interpretable Queries
There is a growing concern about typically opaque decision-making with high-performance machine learning algorithms.
Structured Graph Variational Autoencoders for Indoor Furniture layout Generation
The architecture consists of a graph encoder that maps the input graph to a structured latent space, and a graph decoder that generates a furniture graph, given a latent code and the room graph.
Quantifying Task Complexity Through Generalized Information Measures
In this paper, we propose to measure the complexity of a learning task by the minimum expected number of questions that need to be answered to solve the task.
Neural Network Attributions: A Causal Perspective
We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such).
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.
