DiSK is a generative framework for structured (dictionary-like) data that can handle various data types, from numbers to complex hierarchical types. This model excels in tasks like populating missing data and is especially proficient at predicting numerical values. Its potential extends to augmenting language models for better information retrieval and knowledge manipulation.
What information can we extract from neural networks? Do they tend to learn useful representations that can be interpreted by human experts? In this work, we investigate the learned representations of a model trained on nuclear physics data and show that it captures some of the underlying physical principles.
NEEMo is a technique to fit arbitrary geometries to an arbitrary collection of points. Much like WGANs, it relies on the neural estimation of the Wasserstein metric through the KR dual formulation.
An experiment to demonstrate the non-locality of quantum mechanics through the violation of Bell’s Inequality.