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/

Algorithm Thei Requirinitialx0,i, 1: forj =0 ,1,2,..., 1do 2: Randomlymtraining 3: Compute 4: Update 5: if((j+ 1)p)=0 then 6: Compute 7: Quantize 8: Av 9: Update 10: end 11: end Inthe achie O(1/ p MK)con limited impair gradient 2-bit ratio 32/2 =(if We the communicate issho each parameters.

On the ViT -L/16 architecture, our experiments show that a single adapter, 1.3% of the full model, is able to reach full fine-tuning accuracy on average across 11 challenging downstream classification tasks. Compared with other forms of parameter-efficient adaptation, the isolated nature of the InCA adaptation is computationally desirable for large-scale models. For instance, we adapt ViT -G/14 (1.8B+ parameters) quickly with 20+ adapters in parallel on a single V100 GPU (76% GPU memory reduction) and exhaustively identify its