We determine the sample complexity of pure exploration bandit problems with multiple goodanswers.

Inourexperiments, we visualize images for the mini-ImageNet dataset, and thus we utilize the image generator from the work [10] asG, which is pre-trained on ImageNet, and we pre-trainf on the mini-ImageNet dataset.

Each distribution quantifies the descriptive degrees of various classes given a specific instance.

Theperformance ofneural networksonhigh-dimensional datadistributions suggests that it may be possible to parameterize a representation of agiven highdimensional function with controllably small errors, potentially outperforming standard interpolation methods. We demonstrate, both theoretically and numerically, that this is indeed the case. We map the parameters of a neural network to a system of particles relaxing with an interaction potential determined by the lossfunction.

Thismakesinferenceveryfast,andmaynot even need to use all input featuresto make a prediction (with axis-aligned trees).

Neural Information Processing Systems http://nips.cc/