Talks

PhD Proposal: Hyperdimensional Binary Vector Models for Representation, Integration and Learning of Arbitrary Data

Peter Sutor

4105 Brendan Iribe Center for Computer Science and Engineering (IRB)

Tuesday, December 14, 2021, 10:00 am-12:00 pm

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Abstract

When it comes to various areas of AI and learning, or modalities of data, each enjoys their own unique discipline of machine learning with their own techniques for representing and processing data. As a result, fields such as computer vision have difficulty integrating with fields such as natural language processing. When they do, we find that training generally occurs for each in isolation, or must be tediously made end-to-end by integrating vastly different models before training. This is mostly due to the fact that data has no innate universal representation or even "currency" across the disciplines. The closest we have are real numbered vectors of weights of varying lengths.

In this proposal, we look towards Hyperdimensional Binary Vectors (HBVs) and Hyperdimensional Computing for inspiration to generate a universal currency between modalities and learning paradigms: very long binary vectors of equal lengths. The proposed paradigm represents any unit of data/information as vectors of constant length. Whether something is a pixel, a character, a pattern of pixels/characters, full-blown images and text, or combinations of these - all exist as equally long binary vector embeddings in the same space, that are meaningfully constructed. Our current results show that such representations offer flexibility of representation of any data or feature, beyond even what is typical in each field (for example, irregularly shaped images). The mechanism behind learning semantically meaningful HBVs also allows for rapid learning and inference. We propose to create a general framework to achieve these results for arbitrary information with HBVs, primarily for vision and language, and show that classical machine learning techniques can benefit from such representations. We also propose to develop new methods of inference and learning that rely solely on HBV representations, to investigate the capabilities of HBVs to enable cross-modality fusion of data at an elemental level. As an end product, we propose to deliver an implementation of this framework that can be used with modern machine learning techniques, or as a standalone system for learning via Hyperdimensional Computing.

Examining Committee:

Chair:
Department Representative:
Members:

Dr. Yiannis Aloimonos
Dr. Ramani Duraiswami
Dr. Cornelia Fermuller

Bio

Peter Sutor is a doctoral student in computer science. He is interested in hyperdimensional computing and how it can be applied to modern machine learning and AI techniques to create a universal symbolic encoding of information between vastly different learning architectures, thereby integrating them into a more general, life-long learning system. For example, how can we make an AI that understands language, vision, etc., as arbitrary signals and symbolic representations, and continually learn from them? Other research interests include vector symbolic architectures, vector embeddings, neural network based learning, computer vision, and computational linguistics.

This talk is organized by Tom Hurst