FT-Transformer (Feature Tokenizer + Transformer) is a simple adaptation of the [Transformer](/method/transformer) architecture for the tabular domain. The model (Feature Tokenizer component) transforms all features (categorical and numerical) to tokens and runs a stack of Transformer layers over the tokens, so every Transformer layer operates on the feature level of one object. (This model is similar to [AutoInt](/method/autoint)). In the Transformer component, the `[CLS]` token is appended to $T$. Then $L$ Transformer layers are applied. PreNorm is used for easier optimization and good performance. The final representation of the `[CLS]` token is used for prediction.

**Mechanism Transfer** is a meta-distributional scenario for few-shot domain adaptation in which a data generating mechanism is invariant across domains. This transfer assumption can accommodate nonparametric shifts resulting in apparently different distributions while providing a solid statistical basis for domain adaptation.

Mechanism Transfer

Few-shot Domain Adaptation by Causal Mechanism Transfer

FT-Transformer

Revisiting Deep Learning Models for Tabular Data

**Deterministic Policy Gradient**, or **DPG**, is a policy gradient method for reinforcement learning. Instead of the policy function $\pi\left(.\mid{s}\right)$ being modeled as a probability distribution, DPG considers and calculates gradients for a deterministic policy $a = \mu\_{theta}\left(s\right)$.

Source	Revisiting Deep Learning Models for Tabular Data
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com