Approximating Spatiotemporal Representations Using a 2DCNN

**Dilated Convolutions** are a type of [convolution](https://paperswithcode.com/method/convolution) that “inflate” the kernel by inserting holes between the kernel elements. An additional parameter $l$ (dilation rate) indicates how much the kernel is widened. There are usually $l-1$ spaces inserted between kernel elements. 

Note that concept has existed in past literature under different names, for instance the *algorithme a trous*,  an algorithm for wavelet decomposition (Holschneider et al., 1987; Shensa, 1992).

Dilated Convolution

Multi-Scale Context Aggregation by Dilated Convolutions

HalluciNet

HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

**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 $\mathcal{l}$.

Source	HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com