Enabling Compositional Generalization of AI Systems
Name
li-lishuang-phd-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
29.64 MB
Format
Adobe PDF
Checksum (MD5)
6a15632c4270929480d75ec5d120f15f
Author(s)
Li, Shuang
Advisor(s)
Torralba, Antonio
Date Issued
September 2023
Publisher
Massachusetts Institute of Technology
Abstract
A vital aspect of human intelligence is the ability to compose increasingly complex concepts out of simpler ideas, enabling both rapid learning and adaptation of knowledge. Despite their impressive performance, current AI systems fall short in this area and are often unable to solve tasks that fall outside of their training distribution. The work contained in this thesis aims to bridge this gap by incorporating compositionality into deep neural networks, thereby enhancing their ability to generalize and solve novel and complex tasks, such as generating 2D images and 3D assets based on complicated specifications, or enabling humanoid agents to perform a diverse range of household activities. The implications of this thesis are far-reaching, as compositionality has numerous applications across fields such as biology, robotics, and art production. By significantly improving the compositionality ability of AI systems, this research will pave the way for more data-efficient and powerful models in different research areas.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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