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).

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Severe data imbalance naturally exists among web-scale vision-language datasets.

Generative models have many applications in machine learning. To list a few, they have been usedtogenerate high-fidelity images [26,6],synthesize realistic speech andmusic fragments [58], improve the performance of semi-supervised learning [28, 10], detect adversarial examples and other anomalous data [54], imitation learning [22], and explore promising states in reinforcement learning [41].

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