Complex-valued neural networks (CVNNs) utilize neuron models and operations in the complex-valued domain and are good at handling many complicated scenarios.

There are several reasons why reproducibility is often a challenge in RL.

Recently, large language models (LLMs) ( e.g., GPT -3 and ChatGPT) have shown remarkable zero-shot and few-shot performance on various tasks [

Existing methods on visual knowledge extraction often rely on the predefined format (e.g., sub-verb-obj tuples) or vocabulary (e.g., relation types), restricting the expressiveness of the extracted knowledge. In this work, we take a first exploration to a new paradigm of open visual knowledge extraction.

We propose a general class of sample based explanations of machine learning models, which we term generalized representers . To measure the effect of a training sample on a model's test prediction, generalized representers use two components: a global sample importance that quantifies the importance of the training point to the model and is invariant to test samples, and a local sample importance that measures similarity between the training sample and the test point with a kernel.

Systematic literature reviews (SLRs) play an essential role in summarising, synthe-sising and validating scientific evidence.