**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 $a = \mu\_{theta}\left(s\right)$.

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.

FT-Transformer

Revisiting Deep Learning Models for Tabular Data

In image inpainting task, the mechanism extracts complementary features from the word embedding in two paths by reciprocal attention, which is done by comparing the descriptive text and complementary image areas through reciprocal attention.

Year	2014
Data Source	Papers with Code archive (CC BY-SA)