Large-scale video-language pre-training has shown significant improvement in video-language understanding tasks.

Delusionalbias ariseswhenthe approximation architecture limits the class ofexpressible greedy policies.

Tabular data that is organized in bi-dimensional matrices are widespread in webpages, documents, and databases.

In this work, we move a tiny step towards a theory and better understanding of the representations. Specifically, we study a simpler problem: How similar are the representations learned by two networks with identical architecture but trained from different initializations. We develop a rigorous theory based on the neuron activation subspace match model.

This new task holds significant potential for human-centric applications such as assistive robotics and sports analysis.