5 citations · 11 across the 5 of their papers we have counts for
5 papers
An Evaluation of Memory Optimization Methods for Training Neural Networks
Xiaoxuan Liu, Siddharth Jha, Alvin Cheung
As models continue to grow in size, the development of memory optimization methods (MOMs) has emerged as a solution to address the memory bottleneck encountered when training large…
GACT: Activation Compressed Training for Generic Network Architectures
Xiaoxuan Liu, Lianmin Zheng, Dequan Wang +9
Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint.…
Leveraging Application Data Constraints to Optimize Database-Backed Web Applications
Xiaoxuan Liu, Shuxian Wang, Mengzhu Sun +7
Exploiting the relationships among data is a classical query optimization technique. As persistent data is increasingly being created and maintained programmatically, prior work th…
Long-run User Value Optimization in Recommender Systems through Content Creation Modeling
Akos Lada, Xiaoxuan Liu, Jens Rischbieth +2
Content recommender systems are generally adept at maximizing immediate user satisfaction but to optimize for the \textit{long-run} user value, we need more statistically sophistic…
Order-Preserving Key Compression for In-Memory Search Trees
Huanchen Zhang, Xiaoxuan Liu, David G. Andersen +3
We present the High-speed Order-Preserving Encoder (HOPE) for in-memory search trees. HOPE is a fast dictionary-based compressor that encodes arbitrary keys while preserving their…