At the Annual Conference on Neural Information Processing Systems in December, MIT researchers will present a new way of doing machine learning that enables semantically related concepts to reinforce each other. So, for instance, an object-recognition algorithm would learn to weigh the co-occurrence of the classifications “dog” and “Chihuahua” more heavily than it would the co-occurrence of “dog” and “cat.” In experiments, the researchers found that a machine-learning algorithm that used their training strategy did a better job of predicting the tags that human users applied to images on the Flickr website than it did when it used a conventional training strategy.
“When you have a lot of possible categories, the conventional way of dealing with it is that, when you want to learn a model for each one of those categories, you use only data associated with that category,” says Chiyuan Zhang, an MIT graduate student in electrical engineering and computer science and one of the new paper’s lead authors. “It’s treating all other categories equally unfavorably. Because there are actually semantic similarities between those categories, we develop a way of making use of that semantic similarity to sort of borrow data from close categories to train the model.” Zhang is joined on the paper by his thesis advisor, Tomaso Poggio, the Eugene McDermott Professor in the Brain Sciences and Human Behavior, and by his fellow first author Charlie Frogner, also a graduate student in Poggio’s group. Hossein Mobahi, a postdoc in the Computer Science and Artificial Intelligence Laboratory, and Mauricio Araya-Polo, a researcher with Shell Oil, round out the paper’s co-authors.