Recurrent neural networks (RNNs) notoriously struggle to learn long-term memories, primarily due to vanishing and exploding gradients.

Surgical video-language pretraining (VLP) faces unique challenges due to the knowledge domain gap and the scarcity of multi-modal data.

Exploring every possibility and training a model for each is impractical.

We propose a new algorithm for the problem of recovering data that adheres to multiple, heterogeneous low-dimensional structures from linear observations.

For (3), we relax our proposed test using techniques from robust statistics and imprecise probabilities.

Organoids, specifically, are an active area of research, providing excellent models for human organs and their functions.

Focusing learning on these relevant actions can significantly improve training efficiency and effectiveness.

Deep reinforcement learning (RL) has achieved remarkable successes in complex tasks, e.g Go [

To validate the effectiveness of SHOT, we conduct empirical tests on standard few-shot learning tasks and qualitatively analyze its dynamics.