3 papers
cs.LG2024
SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures
Peimeng Guan, Mark A. Davenport
Inverse problems aim to reconstruct unseen data from corrupted or perturbed measurements. While most work focuses on improving reconstruction quality, generalization accuracy and r…
cs.LG2024
Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures
Peimeng Guan, Naveed Iqbal, Mark A. Davenport +1
Model-based deep learning methods such as loop unrolling (LU) and deep equilibrium model}(DEQ) extensions offer outstanding performance in solving inverse problems (IP). These meth…
stat.ML2023
Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
Austin Xu, Andrew D. McRae, Jingyan Wang +2
We introduce a new type of query mechanism for collecting human feedback, called the perceptual adjustment query ( PAQ). Being both informative and cognitively lightweight, the PAQ…