7 citations · 24 across the 7 of their papers we have counts for
6 papers · 1 filter
Ultra-low Precision Multiplication-free Training for Deep Neural Networks
Chang Liu, Rui Zhang, Xishan Zhang +5
The training for deep neural networks (DNNs) demands immense energy consumption, which restricts the development of deep learning as well as increases carbon emissions. Thus, the s…
Position-Aware Subgraph Neural Networks with Data-Efficient Learning
Chang Liu, Yuwen Yang, Zhe Xie +2
Data-efficient learning on graphs (GEL) is essential in real-world applications. Existing GEL methods focus on learning useful representations for nodes, edges, or entire graphs wi…
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test
Chang Liu, Yuwen Yang, Yue Ding +1
The message-passing scheme is the core of graph representation learning. While most existing message-passing graph neural networks (MPNNs) are permutation-invariant in graph-level…
Completely Heterogeneous Federated Learning
Chang Liu, Yuwen Yang, Xun Cai +2
Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models, and non-i.i.d. labels scenarios. Existing FL methods fail to handle the above three cons…
NoMorelization: Building Normalizer-Free Models from a Sample's Perspective
Chang Liu, Yuwen Yang, Yue Ding +1
The normalizing layer has become one of the basic configurations of deep learning models, but it still suffers from computational inefficiency, interpretability difficulties, and l…
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers
Xishan Zhang, Shaoli Liu, Rui Zhang +8
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers. Recent emerged quantization technique has been applied to inference of deep neur…