**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 $\mathcal{L}$.

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.

RelDiff

RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification

ConvMLP

ConvMLP: Hierarchical Convolutional MLPs for Vision

**NPID** (Non-Parametric Instance Discrimination) is a self-supervision approach that takes a non-parametric classification approach. Noise contrastive estimation is used to learn representations. Specifically, distances (similarity) between instances are calculated directly from the features in a non-parametric way.

Source	ConvMLP: Hierarchical Convolutional MLPs for Vision
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com