GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training

By Krishnateja Killamsetty, Durga Sivasubramanian, Baharan Mirzasoleiman, Ganesh Ramakrishnan, Abir De, Rishabh K. Iyer. 2021

81 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Krishnateja Killamsetty
2. Xujiang Zhao
3. Rishabh K. Iyer
4. F. Chen
5. Xiaojin Zhu

Citations in the corpus

• GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning (2021, neutral): “Several recent works [35, 36, 37, 38, 39] also use coresets to approximate the gradient sum of the individual samples across the entire dataset, for efficient and robust supervised learning.”
• RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning (2021, methodology): “A single epoch here meant a pass over random mini-batches of data points, such that the total number of data points encountered is equal to the size of the coreset of the unlabeled data.”