**Mesh-TensorFlow** is a language for specifying a general class of distributed tensor computations. Where data-parallelism can be viewed as splitting tensors and operations along the "batch" dimension, in Mesh-TensorFlow, the user can specify any tensor dimensions to be split across any dimensions of a multi-dimensional mesh of processors. A MeshTensorFlow graph compiles into a SPMD program consisting of parallel operations coupled with collective communication primitives such as Allreduce.

**Fast Minimum-Norm Attack**, or **FNM**, is a type of adversarial attack that works with different $\ell_{p}$-norm perturbation models ($p=0,1,2,\infty$), is robust to hyperparameter choices, does not require adversarial starting points, and converges within few lightweight steps. It works by iteratively finding the sample misclassified with maximum confidence within an $\ell_{p}$-norm constraint of size $\epsilon$, while adapting $\epsilon$ to minimize the distance of the current sample to the decision boundary.

Fast Minimum-Norm Attack

Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints

Mesh-TensorFlow

Mesh-TensorFlow: Deep Learning for Supercomputers

**Soft Split and Soft Composition** are video frame based operations used in the [FuseFormer](https://paperswithcode.com/method/fuseformer) architecture, specifically the [FuseFormer blocks](https://paperswithcode.com/method/fuseformer-block). We softly split each frame into overlapped patches and then softly composite them back, by using an unfold and fold operator with patch size $k$ being greater than patch stride $s$. When compositing patches back to its original spatial shape, we add up feature values at each overlapping spatial location of neighboring patches.

Source	Mesh-TensorFlow: Deep Learning for Supercomputers
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com