Coreset selection is powerful in reducing computational costs and accelerating data processing for deep learning algorithms. It strives to identify a small subset from large-scale data, so that training only on the subset practically performs on par with full data. When coreset selection is applied in realistic scenes, under the premise that the identified coreset has achieved comparable model performance, practitioners regularly desire the identified coreset can have a size as small as possible for lower costs and greater acceleration. Motivated by this desideratum, for the first time, we pose the problem of "coreset selection with prioritized multiple objectives", in which the smallest coreset size under model performance constraints is e...
Coresets are one of the central methods to facilitate the analysis of large data. We continue a rece...
In the era of datasets of unprecedented sizes, data compression techniques are an attractive approac...
International audienceMulti-core processors employ shared Last Level Caches (LLC). This trend will c...
A wide range of optimization problems arising in machine learning can be solved by gradient descent ...
Motivated by practical generalizations of the classic $k$-median and $k$-means objectives, such as c...
Modern deep learning heavily relies on large labeled datasets, which often comse with high costs in ...
Recent advances in Deep Learning (DL) research have been adopted in a wide variety of applications, ...
The optimization algorithm and its hyperparameters can significantly affect the training speed and r...
A coreset is a small set that can approximately preserve the structure of the original input data se...
Thesis (Ph.D.)--University of Washington, 2018To learn from large datasets, modern machine learning ...
Coresets are succinct summaries of large datasets such that, for a given problem, the solution obtai...
As the push for parallelism continues to increase the number of cores on a chip, and add to the comp...
Constructing small-sized coresets for various clustering problems has attracted significant attentio...
Coresets are among the most popular paradigms for summarizing data. In particular, there exist many ...
Abstract. The target of machine learning is a predictive model that performs well on unseen data. Of...
Coresets are one of the central methods to facilitate the analysis of large data. We continue a rece...
In the era of datasets of unprecedented sizes, data compression techniques are an attractive approac...
International audienceMulti-core processors employ shared Last Level Caches (LLC). This trend will c...
A wide range of optimization problems arising in machine learning can be solved by gradient descent ...
Motivated by practical generalizations of the classic $k$-median and $k$-means objectives, such as c...
Modern deep learning heavily relies on large labeled datasets, which often comse with high costs in ...
Recent advances in Deep Learning (DL) research have been adopted in a wide variety of applications, ...
The optimization algorithm and its hyperparameters can significantly affect the training speed and r...
A coreset is a small set that can approximately preserve the structure of the original input data se...
Thesis (Ph.D.)--University of Washington, 2018To learn from large datasets, modern machine learning ...
Coresets are succinct summaries of large datasets such that, for a given problem, the solution obtai...
As the push for parallelism continues to increase the number of cores on a chip, and add to the comp...
Constructing small-sized coresets for various clustering problems has attracted significant attentio...
Coresets are among the most popular paradigms for summarizing data. In particular, there exist many ...
Abstract. The target of machine learning is a predictive model that performs well on unseen data. Of...
Coresets are one of the central methods to facilitate the analysis of large data. We continue a rece...
In the era of datasets of unprecedented sizes, data compression techniques are an attractive approac...
International audienceMulti-core processors employ shared Last Level Caches (LLC). This trend will c...