**GRLIA** is an incident aggregation framework for online service systems based on graph representation learning over the cascading graph of cloud failures. A representation vector is learned for each unique type of incident in an unsupervised and unified manner, which is able to simultaneously encode the topological and temporal correlations among incidents.

Non-parametric approximation of Q-values by storing all visited states and doing inference through k-Nearest Neighbors.

MFEC

Model-Free Episodic Control

GRLIA

Graph-based Incident Aggregation for Large-Scale Online Service Systems

**Computation Redistribution** is an [neural architecture search](https://paperswithcode.com/task/architecture-search) method for [face detection](https://paperswithcode.com/task/face-detection), which reallocates the computation between the backbone, neck and head of the model based on a predefined search methodology. Directly utilising the backbone of a classification network for scale-specific face detection can be sub-optimal. Therefore, [network structure search](https://paperswithcode.com/method/regnety) is used to reallocate the computation on the backbone, neck and head, under a wide range of flop regimes. The search method is applied to [RetinaNet](https://paperswithcode.com/method/retinanet), with [ResNet](https://paperswithcode.com/method/resnet) as backbone, [Path Aggregation Feature Pyramid Network](https://paperswithcode.com/method/pafpn) (PAFPN)  as the neck and stacked 3 × 3 [convolutional layers](https://paperswithcode.com/method/convolution) for the head. While the general structure is simple, the total number of possible networks in the search space is unwieldy. In the first step, the authors explore the reallocation of the computation within the backbone parts (i.e. stem, C2, C3, C4, and C5), while fixing the neck and head components. Based on the optimised computation distribution on the backbone they find, they further explore the reallocation of the computation across the backbone, neck and head.

Source	Graph-based Incident Aggregation for Large-Scale Online Service Systems
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com