Index
Last updated
Was this helpful?
Last updated
Was this helpful?
- Domingos CACM '12 []
- Sculley et al, NIPS '15 []
Identified the technical debt of Machine Learning system and provided some common mitigation strategy.
* - Smith et al, ICLR '18
Showed that we can increase the batch size, instead of decreasing the learning rate, to get equivalent test accuracies after the same number of training epochs, but with fewer parameter updates, leading to greater parallelism and shorter training times.
- Jiang et al., arXiv '19 []
Proposes an algorithm that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration.
- Yeung et al., CVPR '16
- Fan et al., IJCAI '18
- Wu et al., CVPR '19
- Wu et al., ICCV '19
- Gao et al., CVPR '20
- Liu et al., ACL '20
- Ribeiro et al., ACL '20