Supplementary material A with for numerical features

We provide visual explanation of how embeddings are passed to MLP in Figure 2 and Figure 3. Also, We provide visualisation of target-aware PLE (subsubsection 3.2.2) in Figure 4. Figure 4: Obtaining bins for PLE from decision trees. We used the following datasets: Gesture Phase Prediction (Madeo et al. [27]) Churn Modeling We follow the pointwise approach to learning-to-rank and treat this ranking problem as a regression problem. In this section, we apply the quantile-based piecewise linear encoding (described in subsubsec-tion 3.2.1 to MLP and Transformer on the synthetic GBDT -friendly dataset described in section 5.1 The results are visualized in Figure 5. In this section, we test Fourier features implemented exactly as in Tancik et al. We mostly follow Gorishniy et al. [13] in terms of the tuning, training and evaluation protocols.