A **Deep Boltzmann Machine (DBM)** is a three-layer generative model. It is similar to a [Deep Belief Network](https://paperswithcode.com/method/deep-belief-network), but instead allows bidirectional connections in the bottom layers. Its energy function is  as an extension of the energy function of the RBM:

$$ E\left(v, h\right) = -\sum^{i}\_{i}v\_{i}b\_{i} - \sum^{N}\_{n=1}\sum_{k}h\_{n,k}b\_{n,k}-\sum\_{i, k}v\_{i}w\_{ik}h\_{k} - \sum^{N-1}\_{n=1}\sum\_{k,l}h\_{n,k}w\_{n, k, l}h\_{n+1, l}$$

for a DBM with $N$ hidden layers.

Source: [On the Origin of Deep Learning](https://arxiv.org/pdf/1702.07800.pdf)

**Self-Supervised Temporal Domain Adaptation (SSTDA)** is a method for action segmentation with self-supervised temporal domain adaptation. It contains two self-supervised auxiliary tasks (binary and sequential domain prediction) to jointly align cross-domain feature spaces embedded with local and global temporal dynamics.

SSTDA

Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation

Deep Boltzmann Machine

**Co-Scale Conv-Attentional Image Transformer** (CoaT) is a [Transformer](https://paperswithcode.com/method/transformer)-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other. Second, the conv-attentional mechanism is designed by realizing a relative position embedding formulation in the factorized attention module with an efficient [convolution](https://paperswithcode.com/method/convolution)-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com