Toward more scalable structured models

Messaoud, Safa

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https://hdl.handle.net/2142/110515 Copy

Description

Title

Toward more scalable structured models

Author(s)

Messaoud, Safa

Issue Date

2021-04-21

Director of Research (if dissertation) or Advisor (if thesis)

Schwing, Alexander

Doctoral Committee Chair(s)

Schwing, Alexander

Committee Member(s)

• Forsyth, David
• Do, Minh
• Smaragdis, Paris

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Structured models
• Energy-based models

Abstract

While deep learning has achieved huge success across different disciplines from computer vision and natural language processing to computational biology and physical sciences, training such models is known to require significant amounts of data. One possible reason is that the structural properties of the data and problem are not modeled explicitly. Effectively exploiting the structure can help build more efficient and performing models. The complexity of the structure requires models with enough representation capabilities. However, increased structured model complexity usually leads to increased inference complexity and trickier learning procedures. Also, making progress on real-world applications requires learning paradigms that circumvent the limitation of evaluating the partition function and scale to high-dimensional datasets. In this dissertation, we develop more scalable structured models, i.e., models with inference procedures that can handle complex dependencies between variables efficiently, and learning algorithms that operate in high-dimensional spaces. First, we extend Gaussian conditional random fields, traditionally unimodal and only capturing pairwise variables interactions, to model multi-modal distributions with high-order dependencies between the output space variables, while enabling exact inference and incorporating external constraints at runtime. We show compelling results on the task of diverse gray-image colorization. Then, we introduce a reinforcement learning-based method for solving inference in models with general higher-order potentials, that are intractable with traditional techniques. We show promising results on semantic segmentation. Finally, we propose a new loss, max-sliced score matching (MSSM), for learning structured models at scale. We assess our model on an estimation of densities and scores for implicit distributions in Variational and Wasserstein auto-encoders.

Graduation Semester

2021-05

Type of Resource

Thesis

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http://hdl.handle.net/2142/110515

Copyright and License Information

Copyright 2021 Safa Messaoud

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