As such, it calls for different pre-training methods for specific scenarios, and the pre-trained models are likely to be limited by their universality and representation quality.
To address these issues, we propose a class of matrices (Monarch) that is hardware-efficient (they are parameterized as products of two block-diagonal matrices for better hardware utilization) and expressive (they can represent many commonly used transforms).
We present a new multimodal capsule network that allows us to leverage the strength of capsules in the context of a multimodal learning framework on large amounts of video data.
With this in mind, the descriptions people generate for videos of different dynamic events can greatly improve our understanding of the key information of interest in each video.
Machine learning models are known to perpetuate and even amplify the biases present in the data.