17 citations · 22 across the 7 of their papers we have counts for
7 papers
Preserving Continuous Symmetry in Discrete Spaces: Geometric-Aware Quantization for SO(3)-Equivariant GNNs
Haoyu Zhou, Ping Xue, Hao Zhang +1
Equivariant Graph Neural Networks (GNNs) are essential for physically consistent molecular simulations but suffer from high computational costs and memory bottlenecks, especially w…
Quantized SO(3)-Equivariant Graph Neural Networks for Efficient Molecular Property Prediction
Haoyu Zhou, Ping Xue, Hao Zhang +1
Deploying 3D graph neural networks (GNNs) that are equivariant to 3D rotations (the group SO(3)) on edge devices is challenging due to their high computational cost. This paper add…
IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks
Ping Xue, Yang Lu, Jingfei Chang +2
Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but cause severe accuracy degradation. Existing optimization method…
AIP: Adversarial Iterative Pruning Based on Knowledge Transfer for Convolutional Neural Networks
Jingfei Chang, Yang Lu, Ping Xue +2
With the increase of structure complexity, convolutional neural networks (CNNs) take a fair amount of computation cost. Meanwhile, existing research reveals the salient parameter r…
Self-Distribution Binary Neural Networks
Ping Xue, Yang Lu, Jingfei Chang +2
In this work, we study the binary neural networks (BNNs) of which both the weights and activations are binary (i.e., 1-bit representation). Feature representation is critical for d…
ACP: Automatic Channel Pruning via Clustering and Swarm Intelligence Optimization for CNN
Jingfei Chang, Yang Lu, Ping Xue +2
As the convolutional neural network (CNN) gets deeper and wider in recent years, the requirements for the amount of data and hardware resources have gradually increased. Meanwhile,…