To mitigate this gap, we present a novel Mixed-scale Sparse V oxel Transformer, named MsSVT, which can well capture both types of information simultaneously by the divide-and-conquer philosophy.

This equality is theoretically validated.

Existing work on understanding deep learning often employs measures that compress all data-dependent information into a few numbers.

Tenrec has the potential to become a useful benchmark dataset for a majority of popular recommendation tasks.