However, evaluating whether or not these approximations can be trusted remains a challenge. Most approaches evaluate the posterior estimator only in expectation over the observation space.

How (dis)similar are the learning trajectories of vision-language models and children? Recent modeling work has attempted to understand the gap between models'

The wider application of end-to-end learning methods to embodied decision-making domains remains bottlenecked by their reliance on a superabundance of training data representative of the target domain.

Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained vision models to downstream tasks by tuning a small subset of parameters.

Deep learning models have been widely used to assist doctors with clinical decision-making.