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2406.13283
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Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation
19 June 2024
Bjorn Nieth
Thomas Altstidl
Leo Schwinn
Björn Eskofier
AAML
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Papers citing
"Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation"
6 / 6 papers shown
Title
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
Yulei Qin
Yuncheng Yang
Pengcheng Guo
Gang Li
Hang Shao
Yuchen Shi
Zihan Xu
Yun Gu
Ke Li
Xing Sun
ALM
66
11
0
31 Dec 2024
Efficient Adversarial Training in LLMs with Continuous Attacks
Sophie Xhonneux
Alessandro Sordoni
Stephan Günnemann
Gauthier Gidel
Leo Schwinn
AAML
34
43
0
24 May 2024
Efficient Adversarial Training With Data Pruning
Maximilian Kaufmann
Yiren Zhao
Ilia Shumailov
Robert D. Mullins
Nicolas Papernot
AAML
30
7
0
01 Jul 2022
Dataset Pruning: Reducing Training Data by Examining Generalization Influence
Shuo Yang
Zeke Xie
Hanyu Peng
Minjing Xu
Mingming Sun
P. Li
DD
132
106
0
19 May 2022
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Weizhe Hua
Yichi Zhang
Chuan Guo
Zhiru Zhang
G. E. Suh
OOD
24
14
0
29 Sep 2021
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
Krishnateja Killamsetty
D. Sivasubramanian
Ganesh Ramakrishnan
A. De
Rishabh K. Iyer
OOD
75
184
0
27 Feb 2021
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