**Tofu** is an intra-layer model parallel system that partitions very large DNN models across multiple GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow graph of fine-grained tensor operators used by platforms like MXNet and TensorFlow. To optimally partition different operators in a dataflow graph, Tofu uses a recursive search algorithm that minimizes the total communication cost.

In the space of adversarial perturbation against classifier accuracy, the ARA is the area between a classifier's curve and the straight line defined by a naive classifier's maximum accuracy. Intuitively, the ARA measures a combination of the classifier’s predictive power and its ability to overcome an adversary. Importantly, when contrasted against existing robustness metrics, the ARA takes into account the classifier’s performance against all adversarial examples, without  bounding them by some arbitrary $\epsilon$.

Accuracy-Robustness Area (ARA)

Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness

Tofu

Supporting Very Large Models using Automatic Dataflow Graph Partitioning

**Slanted Triangular Learning Rates (STLR)** is a learning rate schedule which first linearly increases the learning rate and then linearly decays it, which can be seen in Figure to the right. It is a modification of Triangular Learning Rates, with a short increase and a long decay period.

Source	Supporting Very Large Models using Automatic Dataflow Graph Partitioning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com