We introduce the problem of Bayesian optimization with cost-varying variable subsets (BOCVS) where in each iteration, the learner chooses a subset of query variables and specifies their values while the rest are randomly sampled.

Recurrent spiking neural networks (RSNNs) hold great potential for advancing artificial general intelligence, as they draw inspiration from the biological nervous system and show promise in modeling complex dynamics.

Dead leaves - Textures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wetrain with stochastic gradient descent with momentum (set to0.9)for200epochs, starting with a learning rate of0.36 and decaying itby afactor of0.1atepochs155,170and185. The dimensionality of the last and the penultimate embedding are 128 and 4096 respectively. From left to right the columns correspond to the tasks: EuroSAT, Resisc45, Diabetic Retinopathy and Patch Camelyon. Here, wepresent additional data forthese experiments, and provide thefull distributions forthese criteria and all datasets.

Phase-specific wordtopics arediscoveredthathelpexplain theconceptual patterns conditioned on the same phase.