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pro vyhledávání: '"Kaushik, Chiraag"'
Autor:
Ancelin, Brighton, Chen, Yenho, Guan, Peimeng, Kaushik, Chiraag, Martin-Urcelay, Belen, Saad-Falcon, Alex, Singh, Nakul
Learning semantically meaningful image transformations (i.e. rotation, thickness, blur) directly from examples can be a challenging task. Recently, the Manifold Autoencoder (MAE) proposed using a set of Lie group operators to learn image transformati
Externí odkaz:
http://arxiv.org/abs/2409.09542
The classical iteratively reweighted least-squares (IRLS) algorithm aims to recover an unknown signal from linear measurements by performing a sequence of weighted least squares problems, where the weights are recursively updated at each step. Variet
Externí odkaz:
http://arxiv.org/abs/2406.02769
Autor:
Kaushik, Chiraag, Liu, Ran, Lin, Chi-Heng, Khera, Amrit, Jin, Matthew Y, Ma, Wenrui, Muthukumar, Vidya, Dyer, Eva L
Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance. This issue of class bias is widely studied in cases of datasets with sample imbalance, but is relative
Externí odkaz:
http://arxiv.org/abs/2402.11742
The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick. Recent work has demonstrated that in certain suf
Externí odkaz:
http://arxiv.org/abs/2305.02304
Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning. Specific augmentations like translations and scaling in computer vision are traditionally believed to improve generalization by generating new (arti
Externí odkaz:
http://arxiv.org/abs/2210.05021
We consider the problem of sequential graph topology change-point detection from graph signals. We assume that signals on the nodes of the graph are regularized by the underlying graph structure via a graph filtering model, which we then leverage to
Externí odkaz:
http://arxiv.org/abs/2010.11345
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