Training tabular models from scratch for every new problem introduces real workflow friction.
Training tabular models from scratch for every new problem introduces real workflow friction. Our researchers engineered an Adversarial Pre-trained Transformer (APT) that eliminates rigid class limits to deliver zero-shot inference across complex datasets in s
Capital One researchers developed APT (Adversarially Pre-trained Transformer) for zero-shot t...
Capital One researchers developed APT (Adversarially Pre-trained Transformer) for zero-shot tabular prediction. APT uses adversarial synthetic data agents and a mixture block architecture to handle arbitrary class counts with sub-second runtimes.