1/6/14: Steve Gallant


Representing Free Text for Machine Learning Using High-Dimensional, Distributed Vectors

Steve Gallant
MultiModel Research
Cambridge, MA

We want a way to represent natural language, including sentence structure, to make it easy for machine learning (back propagation, perceptron learning). This places certain constraints upon the Representation. Here the Binding problem is especially important; for example, we need to represent the binding of adjectives to the nouns they modify, and nouns to their roles (actor, agent). The talk will present a neurally-inspired representation, MBAT (matrix binding of additive terms), that represents a sentence by a single high-dimensional distributed vector. We argue that this representation satisfies the constraints. It also permits us, to prove that certain concepts can be learned, rather than relying on simulations. A project is underway to test MBAT techniques with real-world problems, such as sentiment analysis. The architecture also has implications for Cognitive Science.

Steve Gallant developed several neural network learning algorithms when Associate Professor of Computer Science at Northeastern University. He showed how to interpret a neural network as an expert system, and was one of the first to extract rules from a neural network. He led a document retrieval project at HNC in San Diego based upon context vectors (which he invented), a precursor to current research that could not represent sentence structure. (HNC created a spinoff based upon this technology.)

Currently Steve leads an NSF supported research project on representation and machine learning at a Cambridge, MA startup: MultiModel Research. He has over 40 publications and a book on “Neural Network Learning.”

Education: MIT: Undergrad (Math), Stanford: Ph.D. (Operations Research), University of Waterloo: Post Doc (Combinatorics & Optimization).

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