Events Calendar

PhD Thesis Proposal - Hanchen Xie

Title: Mitigating Environment Misalignments and Discovering Intrinsic Relations via Symbolic Alignment
 
Date and Time: Tuesday, September 17th, 2024: 2:00pm - 3:00pm
 
Location: SAL 213
 
Committee Members: Yue Wang (Chair), Wael AbdAlamgeed (Outisde), Aram Galstyan, Emilio Ferrara, Peter Beerel
 
Abstract: Modern data-driven machine learning models have achieved remarkable performance on visual recognition tasks, such as object detection and semantic segmentation. Further, when the training data is relatively comprehensive and contains intrinsic relations data, such as object dynamics relations, machine learning models can also discover such relations for downstream tasks, like dynamics prediction. However, unlike data for visual recognition, data that contains comprehensive intrinsic relations can be hard to acquire in many environments (e.g., the real world). As an alternative solution, directly deploying models trained in different environments (e.g., synthetic data) may lead to unsatisfied performance due to environment misalignment challenges. Further, finetuning any pre-trained relations discovery model is also challenging due to the absence of comprehensive data. In this thesis proposal, I will first introduce a set of datasets I designed to investigate the challenge. Then, I will discuss the environment misalignment challenges of conventional machine learning models by using the dynamics prediction tasks as a probe. Following that, I will introduce a promising research direction to mitigate such a challenge by utilizing symbolic space as the common space to align various environments so that the pre-trained relations discovery models can be directly employed and maintain satisfactory performance.
 
Zoom Link: https://usc.zoom.us/j/93657427715?pwd=Q2M5tyfDp7plVXp77nBU11ll3YB7Ul.1 

Location: Henry Salvatori Computer Science Center (SAL) - 213

Audiences: Everyone Is Invited

Contact: Hanchen Xie