We demonstrate that efficient meta-learning can be achieved via end-to-end training of deep neural networks with memory distributed across layers. The persistent state of this memory assumes the entire burden of guiding task adaptation. Moreover, its distributed nature is instrumental in orchestrating adaptation.

In machine teaching, a concept is represented by (and inferred from) a small number of labeled examples.

Neural networks are a key component of the machine learning toolbox.

However, the common assumption both in theoretical and in applied works is that the observed rankings are noisy in the sense that they do not always correspond to the ground-truth ranking.

We hypothesize that the agent's exploration strategy plays a key role in its ability to generalize to new environments.

We review the recent studies in OOD detection, model reprogramming, and backdoor attack. The classification-based methods use the representations extracted from the well-trained classification models in OOD scoring. Matrix to exploit models' detection capability from embedding features; [ Our methods can also be used in the distance-based methods. The term "attack" lies in the fact that, by reprogramming, an attacker can easily In this paper, we also employ the reprogramming property of deep models for transfer learning. Output: learned watermark w .

To address the above issues, we propose to imitate features with Pearson Correlation Coefficient to focus on the relational information from the teacher and relax constraints on the magnitude of the features.