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Lingcn: Structural linearized graph convolutional network for homomorphically encrypted inference
Abstract The growth of Graph Convolution Network (GCN) model sizes has revolutionized
numerous applications, surpassing human performance in areas such as personal …
numerous applications, surpassing human performance in areas such as personal …
Adapi: Facilitating dnn model adaptivity for efficient private inference in edge computing
Private inference (PI) has emerged as a promising solution to execute computations on
encrypted data, safeguarding user privacy and model parameters in edge computing …
encrypted data, safeguarding user privacy and model parameters in edge computing …
[PDF][PDF] Maxk-gnn: Towards theoretical speed limits for accelerating graph neural networks training
In the acceleration of deep neural network training, the graphics processing unit (GPU) has
become the mainstream platform. GPUs face substantial challenges on Graph Neural …
become the mainstream platform. GPUs face substantial challenges on Graph Neural …
Edge-InversionNet: Enabling efficient inference of InversionNet on edge devices
Seismic full waveform inversion (FWI) is a widely used technique in geophysics for inferring
subsurface structures from seismic data. And InversionNet is one of the most successful data …
subsurface structures from seismic data. And InversionNet is one of the most successful data …
Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements
With the rapid proliferation of textual data, predicting long texts has emerged as a significant
challenge in the domain of natural language processing. Traditional text prediction methods …
challenge in the domain of natural language processing. Traditional text prediction methods …