Learning dynamics of deep learning -- force analysis of deep neural networks
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Ren, Yi (Joshua)
issued: 2025-09-25T00:37:08Z
issued: 2025
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: This thesis investigates the learning dynamics of deep learning systems through a local, physics-inspired analytical lens. Motivated by the need for fine-grained insights into model behavior, we begin with the step-wise influence that a single training example exerts on a specific observing example during learning. Central to our approach is the proposed AKG decomposition, which dissects this influence into three interpretable components: similarity (K), normalization (A), and prediction gap (G). This decomposition enables an analogy with classical force analysis: the force originates from G, is shaped by AK, and is ultimately applied to the target object, e.g., to the model confidence, output, hidden representations, or parameters. Building upon this foundation, we gradually scale the analysis from individual interactions to cumulative effects over time, akin to tracking an objectâs motion under multiple forces. We apply it to the following problems. Supervised classification: We study the learning trajectories of examples with varying difficulty and reveal an interesting "zig-zag" pattern that emerges during optimization. Our analysis explains this behavior and inspires a novel knowledge distillation method, Filter-KD, which improves supervision signals for student models. Large language model (LLM) finetuning: We extend the framework to account for the autoregressive nature of LLMs and the presence of negative gradients. The unified perspective explains behaviors across finetuning methods such as SFT, DPO, and GRPO. We also highlight the critical role of negative gradients. In particular, we identify the "squeezing effect": a counterintuitive phenomenon caused by improperly applied gradient ascent. Representation learning: We explore the dynamics of hidden features, revealing how adaptation energy and directions influence the feature drift. Our analysis leads to a provable pattern of feature adaptation in a head-probing then finetuning pipeline, offering insights…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/92432
note: Learning Dynamics of Deep Learningâ Force Analysis of Deep Neural NetworksbyYi (Joshua) RenM.S., University of Edinburgh, UK, 2019B.A., Tongji University, China, 2013A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)September 2025© Yi (Joshua) Ren 2025The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:Learning Dynamics of Deep Learning â Force Analysis of DeepNeural Networks,submitted by Yi (Joshua) Ren in partial fulfillment of the requirementsfor the degree of Doctor of Philosophy in Computer Science.Examining Committee:Danica J. Sutherland, Assistant Professor, Department of Computer Sci-ence, UBCSupervisorMark Schmidt, Professor, Department of Computer Science, UBCSupervisory Committee MemberTrevor Campbell, Associate Professor, Department of Statistics, UBCSupervisory Committee MemberLeonid Sigal, Professor, Department of Computer Science, UBCUniversity ExaminerMuhammad Abdul-Mageed, Associate Professor, School of Information andDepartment of Linguistics, UBCUniversity ExaminerQuanquan Gu, Associate Professor, Department of Computer Science, Uni-versity of California, Los AngelesExternal ExaminerAdditional Supervisory Committee Members:Jeff Clune, Professor, Department of Computer Science, UBCSupervisory Committee MemberAaron Courville, Professor, Department of Computer Science and Opera-tions Research, Universite´ de Montre´al and MilaSupervisory Committee MemberiiAbstractThis thesis investigates the learning dynamics of deep learning systemsthrough a local, physics-inspired analytical lens. Motivated by the needfor fine-grained insights into model behavior, we begin with the step-wiseinfluence that a single training example exerts on a specific observing exam-ple during learning. Central to our a…
has type: Thesis/Dissertation
date issued: 2025-11
is shown at: 10.14288/1.0450240
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Sutherland, Danica J.
type: Text
identifier URI: http://hdl.handle.net/2429/92432