36 citations · 45 across the 3 of their papers we have counts for
3 papers · 1 filter
Accelerating Deep Learning by Focusing on the Biggest Losers
Angela H. Jiang, Daniel L. -K. Wong, Giulio Zhou +8
This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration. Select…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
MLtuner: System Support for Automatic Machine Learning Tuning
Henggang Cui, Gregory R. Ganger, Phillip B. Gibbons
MLtuner automatically tunes settings for training tunables (such as the learning rate, the momentum, the mini-batch size, and the data staleness bound) that have a significant impa…