In image classification, it is common to utilize a pretrained model to extract meaningful features of the input images, and then to train a classifier on top of it to make predictions for any downstream task.

We help address these challenges through three contributions. First, we publish a new dataset, EHRSHOT, which contains de-identified structured data from the electronic health records (EHRs) of 6,739 patients from Stanford Medicine.

However, zero-shot learning methods still require human instruction set to solve a new task.

Unlike the case for text--wherein every document written in a given language shares a basic lexicon for tokenization--there is no one-to-one correspondence between neurons in different individuals.

MIM to enhance the adversarial robustness of downstream models. It is important to highlight that our paper's focus is specifically on the adversarial robustness of ViTs. It is shown that our method can provide an effective defense against severe adversarial attacks. We propose two hypotheses for explaining the reason behind our method's effectiveness: (1) Given Figure 3 (a) shows the comparison between the results of noise being known and unknown. When the attacker can access the noise, our model's robust accuracy does not improve much as The results indicate that both proposed hypotheses are true.