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HYDRA: Pruning Adversarially Robust Neural Networks
While the research community has extensively explored the use of robust training and network pruning independently to address one of these challenges, only a few recent works have studied them jointly.
Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests
We summarize our contributions as follows: 1.
Appendix A Implementation of Taylor Expansion on Unit Hamming Sphere
Follow the discussion in Section 3.1.2 In this section, we continue the discussion in Section 3.3 and obtain the form used in (25). The architecture of the discriminator is shown in Table 3. Exponential Linear Units [ One of the issue with BLEU is that in the case that a higher order n-gram precision of a sentence is 0, then the BLEU score will be 0, resulting in severely underestimation. This is due to the fact that BLEU is calculated by the geometric mean of precision. Sentences in the COCO dataset have a maximum length of 24 tokens and a vocabulary of 4.6k Training and validation data both consist of 10k sentences.
AI tool that speeds up patient discharges trialled by NHS
An artificial intelligence tool designed to speed up the discharge of patients is being trialled at a hospital trust in London. The platform completes documents needed to send fit patients home, potentially saving hours of delays and freeing up beds. Wes Streeting, the health secretary, said the tech will enable doctors to spend less time on paperwork and more time focused on care, cutting waiting times in the process. The platform, which is being piloted at Chelsea and Westminster NHS trust, extracts information from medical records, including diagnoses and test results. This helps medics to draft discharge summaries, which have to be completed before a person is sent home from hospital.
A Related Work In this section, we briefly review few-shot learning (FSL) and two domain adaptation settings related
Existing FSL methods can be divided into three categories: (1) Augmenting training data set by prior knowledge. Data augmentation via hand-crafted rules serves as pre-processing in FSL methods. Note that our method belongs to category (1). In the hypothesis transfer learning (HTL), we can only access a well-trained source-domain classifier and small labeled or abundant unlabeled target data. Compared with FHA, HTL still requires at least small target data (e.g., at least We state here two known generalization bounds [5] used in our proof.
