RelDiff generates entity-relation-entity embeddings in a single embedding space. RelDiff adopts two fundamental vector algebraic operators to transform entity and relation embeddings from knowledge graphs into entity-relation-entity embeddings. In particular, RelDiff can encode finer-grained information about the relations than is captured when separate embeddings are learned for the entities and the relations.

**Sarsa_INLINE_MATH_1** extends eligibility-traces to action-value methods. It has the same update rule as for **TD_INLINE_MATH_1** but we use the action-value form of the TD erorr:

$$ \delta\_{t} = R\_{t+1} + \gamma\hat{q}\left(S\_{t+1}, A\_{t+1}, \mathbb{w}\_{t}\right) - \hat{q}\left(S\_{t}, A\_{t}, \mathbb{w}\_{t}\right) $$

and the action-value form of the [eligibility trace](https://paperswithcode.com/method/eligibility-trace):

$$ \mathbb{z}\_{-1} = \mathbb{0} $$

$$ \mathbb{z}\_{t} = \gamma\lambda\mathbb{z}\_{t-1} + \nabla\hat{q}\left(S\_{t}, A\_{t}, \mathbb{w}\_{t} \right), 0 \leq t \leq T$$

Source: Sutton and Barto, Reinforcement Learning, 2nd Edition

Sarsa Lambda

RelDiff

RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification

**ConvMLP** is a hierarchical convolutional MLP for visual recognition, which consists of a stage-wise, co-design of [convolution](https://paperswithcode.com/method/convolution) layers, and MLPs. The Conv Stage consists of $C$ convolutional blocks with $1\times 1$ and $3\times 3$ kernel sizes. It is repeated $M$ times before a down convolution is utilized to express a level $L$. The MLP-Conv Stage consists of Channelwise MLPs, with skip layers, and a [depthwise convolution](https://paperswithcode.com/method/depthwise-convolution). This is repeated $M$ times before a down convolution is utilized to express a level $\mathcal{L}$.

Source	RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com