5 citations · 25 across the 14 of their papers we have counts for
7 papers · 1 filter
Perseus: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models
Matthew LeMay, Shijian Li, Tian Guo
Deep learning models are increasingly used for end-user applications, supporting both novel features such as facial recognition, and traditional features, e.g. web search. To accom…
Inverse Reinforcement Learning with Multiple Ranked Experts
Pablo Samuel Castro, Shijian Li, Daqing Zhang
We consider the problem of learning to behave optimally in a Markov Decision Process when a reward function is not specified, but instead we have access to a set of demonstrators o…
FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control
Longxiang Shi, Shijian Li, Longbing Cao +3
In recent years significant progress has been made in dealing with challenging problems using reinforcement learning.Despite its great success, reinforcement learning still faces c…
TBQ(): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning
Longxiang Shi, Shijian Li, Longbing Cao +2
Off-policy reinforcement learning with eligibility traces is challenging because of the discrepancy between target policy and behavior policy. One common approach is to measure the…
Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games
Li Zhang, Wei Wang, Shijian Li +1
Researchers on artificial intelligence have achieved human-level intelligence in large-scale perfect-information games, but it is still a challenge to achieve (nearly) optimal resu…
Speeding up Deep Learning with Transient Servers
Shijian Li, Robert J. Walls, Lijie Xu +1
Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce the training time of deep learning models by using a cluster of GPU servers. While such sp…