Neural networks often exhibit emergent behavior, where qualitatively new capabilities arise from scaling up the amount of parameters, training data, or training steps. One approach to understanding emergence is to find continuous \textit{progress measures} that underlie the seemingly discontinuous...

Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the latent space should depend only on the task, the data, the loss, and other architecture-specific constraints. However...

Existing benchmarks for evaluating long video understanding falls short on multiple aspects, either lacking in scale or quality of annotations. These limitations arise from the difficulty in collecting dense annotations for long videos (e.g. actions, dialogues, etc.), which are often obtained by...