12 citations · 24 across the 4 of their papers we have counts for
4 papers
Dataset Distillation by Automatic Training Trajectories
Dai Liu, Jindong Gu, Hu Cao +2
Dataset Distillation is used to create a concise, yet informative, synthetic dataset that can replace the original dataset for training purposes. Some leading methods in this domai…
Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach
Urvij Saroliya, Eishi Arima, Dai Liu +1
GPU-based heterogeneous architectures are now commonly used in HPC clusters. Due to their architectural simplicity specialized for data-level parallelism, GPUs can offer much highe…
Orchestrated Co-scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning
Issa Saba, Eishi Arima, Dai Liu +1
CPU-GPU heterogeneous architectures are now commonly used in a wide variety of computing systems from mobile devices to supercomputers. Maximizing the throughput for multi-programm…
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments
Rui Song, Dai Liu, Dave Zhenyu Chen +4
In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often lea…