Video quality can suffer from limited internet speed while being streamed by users.
AMPER replaces the widely-used time-costly tree-traversal-based priority sampling in PER while preserving the learning performance.
Memory augmented neural network has been proposed to achieve the goal, but the memory module has to be stored in an off-chip memory due to its size.
Moreover, this approach achieves accuracies comparable to floating-point precision implementations in software for NN classification and one/few-shot learning tasks.
Extracting large amounts of data from biological samples is not feasible due to radiation issues, and image processing in the small-data regime is one of the critical challenges when working with a limited amount of data.