Deep Transfer: A Markov Logic Approach

and apply it to an entirely different one. For example, Wall Street firms often hire physicists to solve finance problems. Even though these two domains have superficially nothing in common, training as a physicist provides knowledge and skills that are highly applicable in finance (for example, solving differential equations and performing Monte Carlo simulations). Yet standard machine-learning approaches are unable to do this. For example, a model learned on physics data would not be applicable to finance data, because the variables in the two domains are different. Despite the recent interest in transfer learning, most approaches do not address this problem, instead focusing on modeling either a change of distributions over the same variables or minor variations of the same domain (for example, different numbers of objects). We call this shallow transfer. Our goal is to perform deep transfer, which involves generalizing across different domains (that is, between domains with different objects, classes, properties, and relations). Performing deep transfer requires discovering structural regularities that apply to many different domains, irrespective of their superficial descriptions. For example, two domains may be modeled by the same type of equation, and solution techniques learned in one can be applied in the other. The inability to do this is arguably the biggest gap between current learning systems and humans. Articles