1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.DC2023★ 1 cited
KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training
Truong Thao Nguyen, Balazs Gerofi, Edgar Josafat Martinez-Noriega +2
This paper proposes a method for hiding the least-important samples during the training of deep neural networks to increase efficiency, i.e., to reduce the cost of training. Using…
cs.CV2022★ 1 cited
Pre-training Vision Transformers with Formula-driven Supervised Learning
Hirokatsu Kataoka, Sora Takashima, Ryo Hayamizu +6
In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k and can approach that of the JFT-300M d…