most citedThe Limits of Perception: Analyzing Inconsistencies in Saliency Maps in XAI

1 citations · 2 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Studying quantization trade-offs for efficient inference deployment in machine translation

Jim Zhao, Sohir Maskey, Koen Oostermeijer +2

Deploying large language models in realistic server environments poses challenges, as the system needs to provide high-quality responses with low latency. Quantization is a common…

cs.LG2026

Optimizer choice matters for the emergence of Neural Collapse

Jim Zhao, Tin Sum Cheng, Wojciech Masarczyk +1

Neural Collapse (NC) refers to the emergence of highly symmetric geometric structures in the representations of deep neural networks during the terminal phase of training. Despite…

cs.LG2024

Cubic regularized subspace Newton for non-convex optimization

Jim Zhao, Aurelien Lucchi, Nikita Doikov

This paper addresses the optimization problem of minimizing non-convex continuous functions, which is relevant in the context of high-dimensional machine learning applications char…

eess.IV2024

A CT Image Denoising Method with Residual Encoder-Decoder Network

Helena Shawn, Thompson Chyrikov, Jacob Lanet +3

Utilizing a low-dose CT approach significantly reduces the radiation exposure for patients, yet it introduces challenges, such as increased noise and artifacts in the resultant ima…

cs.CV20241 cited

The Limits of Perception: Analyzing Inconsistencies in Saliency Maps in XAI

Anna Stubbin, Thompson Chyrikov, Jim Zhao +1

Explainable artificial intelligence (XAI) plays an indispensable role in demystifying the decision-making processes of AI, especially within the healthcare industry. Clinicians rel…

eess.IV20241 cited

A Spectrum-based Image Denoising Method with Edge Feature Enhancement

Peter Luvton, Alfredo Castillejos, Jim Zhao +1

Image denoising stands as a critical challenge in image processing and computer vision, aiming to restore the original image from noise-affected versions caused by various intrinsi…