A gradient-attention explainability method produces sequence-level visual and textual saliency maps for free-form answers from large vision-language models, with stronger human-attention alignment and faithfulness than prior baselines.
Mortality Forecasting using Variational Inference
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abstract
This paper considers the problem of forecasting mortality rates. A large number of models have already been proposed for this task, but they generally have the disadvantage of either estimating the model in a two-step process, possibly losing efficiency, or relying on methods that are cumbersome for the practitioner to use. We instead propose using variational inference and the probabilistic programming library Pyro for estimating the model. This allows for flexibility in modelling assumptions while still being able to estimate the full model in one step. The models are fitted on Swedish mortality data and we find that the in-sample fit is good and that the forecasting performance is better than other popular models. Code is available at https://github.com/LPAndersson/VImortality.
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GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models
A gradient-attention explainability method produces sequence-level visual and textual saliency maps for free-form answers from large vision-language models, with stronger human-attention alignment and faithfulness than prior baselines.