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Found 5 papers, 2 papers with code
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.
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.
Information Pursuit: A Bayesian Framework for Sequential Scene Parsing
Despite enormous progress in object detection and classification, the problem of incorporating expected contextual relationships among object instances into modern recognition systems remains a key challenge.
Vantage Feature Frames for Fine-Grained Categorization
We study fine-grained categorization, the task of distinguishing among (sub)categories of the same generic object class (e. g., birds), focusing on determining botanical species (leaves and orchids) from scanned images.
