reSGLD proposes to simulate a high-temperature particle for exploration and a low-temperature particle for exploitation and allows them to swap simultaneously. Moreover, a correction term is included to avoid biases.

**RepVGG** is a [VGG](https://paperswithcode.com/method/vgg)-style convolutional architecture. It has the following advantages:

- The model has a VGG-like plain (a.k.a. feed-forward) topology 1 without any branches. I.e., every layer takes
the output of its only preceding layer as input and feeds the output into its only following layer.
- The model’s body uses only 3 × 3 conv and [ReLU](https://paperswithcode.com/method/relu).
- The concrete architecture (including the specific depth and layer widths) is instantiated with no automatic
search, manual refinement, compound scaling, nor other heavy designs.

RepVGG

RepVGG: Making VGG-style ConvNets Great Again

reSGLD

Non-convex Learning via Replica Exchange Stochastic Gradient MCMC

**BS-Net** is an architecture for COVID-19 severity prediction based on clinical data from different modalities. The architecture comprises 1) a shared multi-task feature extraction backbone, 2) a lung segmentation branch, 3) an original registration mechanism that acts as a ”multi-resolution feature alignment” block operating on the encoding backbone , and 4) a multi-regional classification part for the final six-valued score estimation. 

All these blocks act together in the final training thanks to a loss specifically crated for this task. This loss guarantees also performance robustness, comprising a differentiable version of the target discrete metric. The learning phase operates in a weakly-supervised fashion. This is due to the fact that difficulties and pitfalls in the visual interpretation of the disease signs on CXRs (spanning from subtle findings to heavy lung impairment), and the lack of detailed localization information, produces unavoidable inter-rater variability among radiologists in assigning scores.

Specifically the architectural details are:

- The input image is processed with a convolutional backbone; the authors opt for a [ResNet](https://paperswithcode.com/method/resnet)-18.
- Segmentation is performed by a nested version of [U-Net](https://paperswithcode.com/method/u-net) (U-Net++).
- Alignment is estimated through the segmentation probability map produced by the U-Net++ decoder, which is achieved through a [spatial transformer network](https://paperswithcode.com/method/spatial-transformer) -- able to estimate the spatial transform matrix in order to center, rotate, and correctly zoom the lungs. After alignment at various scales, features are forward to a [ROIPool](https://paperswithcode.com/method/roi-pooling). 
- The alignment block is pre-trained on the synthetic alignment dataset in a weakly-supervised setting, using a Dice loss.
- The scoring head uses [FPNs](https://paperswithcode.com/method/fpn) for the combination of multi-scale feature maps. The multiresolution feature aligner produces input feature maps that are well focused on the specific area of interest. Eventually, the output of the FPN layer flows in a series of convolutional blocks to retrieve the output map. The classification is performed by a final [Global Average Pooling](https://paperswithcode.com/method/global-average-pooling) layer and a [SoftMax](https://paperswithcode.com/method/softmax) activation.
- The Loss function used for training is a sparse categorical cross entropy (SCCE) with a (differentiable) mean absolute error contribution.

Source	Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Year	2000
Data Source	Papers with Code archive (CC BY-SA)