297 citations · 1.2k across the 113 of their papers we have counts for
21 papers · 1 filter
KAN or MLP: A Fairer Comparison
Runpeng Yu, Weihao Yu, Xinchao Wang
This paper does not introduce a novel method. Instead, it offers a fairer and more comprehensive comparison of KAN and MLP models across various tasks, including machine learning,…
Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching
Xinyin Ma, Gongfan Fang, Michael Bi Mi +1
Diffusion Transformers have recently demonstrated unprecedented generative capabilities for various tasks. The encouraging results, however, come with the cost of slow inference, s…
Ungeneralizable Examples
Jingwen Ye, Xinchao Wang
The training of contemporary deep learning models heavily relies on publicly available data, posing a risk of unauthorized access to online data and raising concerns about data pri…
Distilled Datamodel with Reverse Gradient Matching
Jingwen Ye, Ruonan Yu, Songhua Liu +1
The proliferation of large-scale AI models trained on extensive datasets has revolutionized machine learning. With these models taking on increasingly central roles in various appl…
Generator Born from Classifier
Runpeng Yu, Xinchao Wang
In this paper, we make a bold attempt toward an ambitious task: given a pre-trained classifier, we aim to reconstruct an image generator, without relying on any data samples. From…
PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation
Dapeng Hu, Jian Liang, Xinchao Wang +1
Unsupervised domain adaptation (UDA) has witnessed remarkable advancements in improving the accuracy of models for unlabeled target domains. However, the calibration of predictive…