**StreaMRAK** is a streaming version of kernel ridge regression. It divdes the problem into several levels of resolution, which allows continual refinement to the predictions.

**Progressive Neural Architecture Search**, or **PNAS**, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to complex ones, pruning out unpromising structures as we go. 

At iteration $b$ of the algorithm, we have a set of $K$ candidate cells (each of size $b$ blocks), which we train and evaluate on a dataset of interest. Since this process is expensive, PNAS also learns a model or surrogate function which can predict the performance of a structure without needing to train it. We then expand the $K$ candidates of size $b$ into $K' \gg K$ children, each of size $b+1$. The surrogate function is used to rank all of the $K'$ children, pick the top $K$, and then train and evaluate them. We continue in this way until $b=B$, which is the maximum number of blocks we want to use in a cell.

PNAS

Progressive Neural Architecture Search

StreaMRAK

StreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm

Our tracker, CenterTrack, applies a detection model to a pair of images and detections from the prior frame. Given this minimal input, CenterTrack localizes objects and predicts their associations with the previous frame. That's it. CenterTrack is simple, online (no peeking into the future), and real-time.

Source	StreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com