GMED-editedexamplesremain similar to their unedited forms, but can yield increased loss in the upcoming model updates, thereby making thefuture replays more effectiveinovercoming catastrophic forgetting.

Theoretically,Falconn++ asymptotically achieves lower query time complexity than Falconn, an optimal locality-sensitive hashing scheme on angular distance.

Surveillance systems [HC11] are equipped with a suite of sensors that can be switched and steered to focus attention on any target or location over a physical landscape (see Figure 1) to detect abrupt changes at any location. On the other hand, sensor suites are resource limited, and only a limited subset, among all the locations, can be probed at any time.

In this paper, we propose an efficient and effective training scheme coined as Sparse SAM (SSAM), which achieves sparse perturbation by a binary mask.

This step indicates positions that are different across reads and thus infers the potential locations of SNPs. All other positions are discarded to obtain the SNP matrix (Figure 1c).

That said, there often exist environments that resemble in structure (dynamics) yet provide more accessible rollouts (eg, unlimited in simulators).