24 citations · 34 across the 5 of their papers we have counts for
9 papers
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding
Sai Qian Zhang, Bradley McDanel, H. T. Kung
Block Floating Point (BFP) can efficiently support quantization for Deep Neural Network (DNN) training by providing a wide dynamic range via a shared exponent across a group of val…
Succinct and Robust Multi-Agent Communication With Temporal Message Control
Sai Qian Zhang, Jieyu Lin, Qi Zhang
Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL). Howe…
Term Revealing: Furthering Quantization at Run Time on Quantized DNNs
H. T. Kung, Bradley McDanel, Sai Qian Zhang
We present a novel technique, called Term Revealing (TR), for furthering quantization at run time for improved performance of Deep Neural Networks (DNNs) already quantized with con…
On the Robustness of Cooperative Multi-Agent Reinforcement Learning
Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang +2
In cooperative multi-agent reinforcement learning (c-MARL), agents learn to cooperatively take actions as a team to maximize a total team reward. We analyze the robustness of c-MAR…
RTN: Reparameterized Ternary Network
Yuhang Li, Xin Dong, Sai Qian Zhang +3
To deploy deep neural networks on resource-limited devices, quantization has been widely explored. In this work, we study the extremely low-bit networks which have tremendous speed…
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
Sai Qian Zhang, Qi Zhang, Jieyu Lin
Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications. However, achieving effic…