Second, we introduce a novel loss to explicitly enforce consistency across generated views both in space and in time.
We propose a two-stage experimental design, where the two brands only need to agree on high-level aggregate parameters of the experiment to test the alternate experiences.
To this end, we present VideoSham, a dataset consisting of 826 videos (413 real and 413 manipulated).
We propose a method to detect individualized highlights for users on given target videos based on their preferred highlight clips marked on previous videos they have watched.
We train our network to map the activity- and interaction-based latent structural representations of the different modalities to per-frame highlight scores based on the representativeness of the frames.
Earlier works on optimal bidding strategy apply model-based batch reinforcement learning methods which can not generalize to unknown budget and time constraint.
The highest bidding advertiser wins but pays only the second-highest bid (known as the winning price).
Factorization Machines (FMs) is an important supervised learning approach due to its unique ability to capture feature interactions when dealing with high-dimensional sparse data.