**TransE** is an energy-based model that produces knowledge base embeddings. It models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Relationships are represented as translations in the embedding space: if $\left(h, \mathcal{l}, t\right)$ holds, the embedding of the tail entity $t$ should be close to the embedding of the head entity $h$ plus some vector that depends on the relationship $\mathcal{l}$.

Approximating Spatiotemporal Representations Using a 2DCNN

HalluciNet

HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

TransE

Translating Embeddings for Modeling Multi-relational Data

**Continuously Indexed Domain Adaptation** combines traditional adversarial adaptation with a novel discriminator that models the encoding-conditioned domain index distribution.

Image Source: [Wang et al.](https://arxiv.org/pdf/2007.01807v2.pdf)

Source	Translating Embeddings for Modeling Multi-relational Data
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com