28 citations · 70 across the 16 of their papers we have counts for
6 papers · 1 filter
A Super-pixel-based Approach to the Stable Interpretation of Neural Networks
Shizhan Gong, Jingwei Zhang, Qi Dou +1
Saliency maps are widely used in the computer vision community for interpreting neural network classifiers. However, due to the randomness of training samples and optimization algo…
Towards a Scalable Reference-Free Evaluation of Generative Models
Azim Ospanov, Jingwei Zhang, Mohammad Jalali +3
While standard evaluation scores for generative models are mostly reference-based, a reference-dependent assessment of generative models could be generally difficult due to the una…
MoreauPruner: Robust Pruning of Large Language Models against Weight Perturbations
Zixiao Wang, Jingwei Zhang, Wenqian Zhao +2
Few-shot gradient methods have been extensively utilized in existing model pruning methods, where the model weights are regarded as static values and the effects of potential weigh…
Sparse Domain Transfer via Elastic Net Regularization
Jingwei Zhang, Farzan Farnia
Transportation of samples across different domains is a central task in several machine learning problems. A sensible requirement for domain transfer tasks in computer vision and l…
Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach
Jingwei Zhang, Mohammad Jalali, Cheuk Ting Li +1
A fine-grained comparison of generative models requires the identification of sample types generated differently by each of the involved models. While quantitative scores have been…
An Interpretable Evaluation of Entropy-based Novelty of Generative Models
Jingwei Zhang, Cheuk Ting Li, Farzan Farnia
The massive developments of generative model frameworks require principled methods for the evaluation of a model's novelty compared to a reference dataset. While the literature has…