140 citations · 719 across the 50 of their papers we have counts for
6 papers · 1 filter
Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics
Ruoyang Liu, Chenhan Wei, Yixiong Yang +3
Data quantization is an effective method to accelerate neural network training and reduce power consumption. However, it is challenging to perform low-bit quantized training: the c…
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
Jiayu Chen, Yuanxin Zhang, Yuanfan Xu +5
We introduce a curriculum learning algorithm, Variational Automatic Curriculum Learning (VACL), for solving challenging goal-conditioned cooperative multi-agent reinforcement learn…
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness
Yi Cai, Xuefei Ning, Huazhong Yang +1
Adversarial attacks have rendered high security risks on modern deep learning systems. Adversarial training can significantly enhance the robustness of neural network models by sup…
Exploring the Potential of Low-bit Training of Convolutional Neural Networks
Kai Zhong, Xuefei Ning, Guohao Dai +5
In this work, we propose a low-bit training framework for convolutional neural networks, which is built around a novel multi-level scaling (MLS) tensor format. Our framework focuse…
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS
Xuefei Ning, Yin Zheng, Tianchen Zhao +2
This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search. Specifically, different from ex…
A Deep Learning Approach for Blind Drift Calibration of Sensor Networks
Yuzhi Wang, Anqi Yang, Xiaoming Chen +3
Temporal drift of sensory data is a severe problem impacting the data quality of wireless sensor networks (WSNs). With the proliferation of large-scale and long-term WSNs, it is be…