In this natural setting, it is not common to see the same data sample multiple times, or even twice at times.

The hippocampus plays a critical role in learning, memory, and spatial representation, processes that depend on the NMDA receptor (NMDAR).

In this part, we show the I/O speed in the synchronous and asynchronous cases. Figure 3a show the I/O speed for four jobs that start at different moments. Then we further compare the RefCnt with the generic cache policy in the above cases. D = sample ([0, 13333], 10000) means sample a subset D with 10000 of size from [0, 13333] uniformly at random 36th Conference on Neural Information Processing Systems (NeurIPS 2022). DSA can always get the minimum misses.

Prior work tries to improve the sampling locality by enforcing all the training jobs loading the same dataset in the same order and pace. However, such a solution isonly efficient under strong constraints: alljobs are trained onthe same dataset with the same starting moment and training speed. In this paper, we propose a new data loading method for efficiently training parallel DNNs with much flexible constraints. Our method is still highly efficient when different training jobs use different but overlapped datasets and have different starting moments andtrainingspeeds.

To enable models to learn better representation spaces that simulate the real world, the larger the dataset and the richer the physical knowledge contained inthe videos, the better the training effect. Researchers often construct these large-scale datasets through web scraping.