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Found 5 papers, 3 papers with code
Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective
Given the fixed point equation (FPE) derived from the variational inference on the Markov random fields, the deep GNNs, including JKNet, GCNII, DGCN, and the classical GNNs, such as GCN, GAT, and APPNP, can be regarded as different approximations of the FPE.
Soft Threshold Ternary Networks
The latest STTN shows that ResNet-18 with ternary weights and ternary activations achieves up to 68. 2% Top-1 accuracy on ImageNet.
APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers
Showing how vision Transformers are at the risk of privacy leakage via gradients, we urge the significance of designing privacy-safer Transformer models and defending schemes.
Joint Channel and Weight Pruning for Model Acceleration on Moblie Devices
In this paper, we present a unified framework with Joint Channel pruning and Weight pruning (JCW), and achieves a better Pareto-frontier between the latency and accuracy than previous model compression approaches.
Architecture Aware Latency Constrained Sparse Neural Networks
Acceleration of deep neural networks to meet a specific latency constraint is essential for their deployment on mobile devices.
