Deep Transfer: A Markov Logic Approach

We argue that second-order Markov logic is ideally suited for this purpose and propose an approach based on it. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables and instantiates these formulas with predicates from the target domain. Our approach has successfully transferred learned knowledge among molecular biology, web, and social network domains. 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).