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Found 7 papers, 1 papers with code
BloomVQA: Assessing Hierarchical Multi-modal Comprehension
We propose a novel VQA dataset, BloomVQA, to facilitate comprehensive evaluation of large vision-language models on comprehension tasks.
Uncertainty Propagation through Trained Deep Neural Networks Using Factor Graphs
Our implementation balances the benefits of sampling and analytical propagation techniques, which we believe, is a key factor in achieving performance improvements.
Confidence Calibration for Systems with Cascaded Predictive Modules
Existing conformal prediction algorithms estimate prediction intervals at target confidence levels to characterize the performance of a regression model on new test samples.
Unpacking Large Language Models with Conceptual Consistency
The success of Large Language Models (LLMs) indicates they are increasingly able to answer queries like these accurately, but that ability does not necessarily imply a general understanding of concepts relevant to the anchor query.
Confidence Calibration for Domain Generalization under Covariate Shift
Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation.
Modular Adaptation for Cross-Domain Few-Shot Learning
Adapting pre-trained representations has become the go-to recipe for learning new downstream tasks with limited examples.
Learning Compositional Visual Concepts with Mutual Consistency
Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data.
