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.

Our TN implementation accelerates a recently-proposed KFAC variant up to 4.5 x while removing the

Despite efficiencyimprovements, existing methods overlook themisguidance in anchors learning induced by partial missing samples,i.e., the absence of samples results in shift of learned anchors, further leading to sub-optimal clustering performance.

Pruning is one of the predominant approaches for compressing deep neural networks (DNNs).

Several approaches have been proposed to remedy the edacious nature of archetypal analysis, proposing, e.g.,efficient active-set quadratic programming (Chen etal.,2014),