This stands in sharp contrast to fully connected networks, which are not universal approximators if their width is the input dimensiond[21,11].

This task has received a considerable amount of attention, particularly for classical ways of modeling distributions like structured prediction.

Program synthesis of general-purpose source code from natural language specifi-cations is challenging due to the need to reason about high-level patterns in thetarget program and low-level implementation details at the same time.

Inrealisticscenarios with very limited numbers of data samples, it can be challenging to identify a kernel powerful enough to distinguish complex distributions.

In this paper, we study the problem of unsupervised object segmentation from single images.

In experiments, VAEs trained with the new loss function generatedrealistic,high-qualityimagesamples.

However, much less effort has been devoted to exploring the adversarial robustness of pre-trained models.

SynCx outperforms or is strongly competitive with current models for unsupervised object discovery.

A core task in multi-modal learning is to integrate information from multiple feature spaces (e.g., text and audio), offering modality-invariant essential representationsofdata.