Understanding and Improving Representational Robustness of Machine Learning Models
The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public. In this thesis, we will do a systematic study on the understanding and improvement of several machine learning models, including smoothed models and generic representation...
Main Author: | Ko, Ching-Yun |
---|---|
Other Authors: | Daniel, Luca |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2024
|
Online Access: | https://hdl.handle.net/1721.1/156297 |
Similar Items
-
Probing, Improving, and Verifying Machine Learning Model Robustness
by: Xiao, Kai Yuanqing
Published: (2023) -
Improved Multiple Vector Representations of Images and Robust Dictionary Learning
by: Chengchang Pan, et al.
Published: (2022-03-01) -
An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
by: Gagan Preet Kour Marwah, et al.
Published: (2022-10-01) -
Representations and strategies for transferable machine learning improve model performance in chemical discovery
by: Harper, Daniel R, et al.
Published: (2022) -
Understanding representation learning for deep reinforcement learning
by: Le Lan, C
Published: (2023)