TreeV AE hierarchically divides samples according to their intrinsic characteristics, shedding light on hidden structures in the data.

Current deep networks are very data-hungry and benefit from training on large-scale datasets, which are often time-consuming to collect and annotate.

We revisit this scenario as an experimental case study in Section 6.

Figure 1: Evaluation of T abEBM and other state-of-the-art tabular generative methods across six key metrics (larger area indicates better performance).