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Found 3 papers, 1 papers with code
Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation
The fine-grained structured sparsity causes a large amount of unnecessary operations during both matrix multiplication and latent factor update, increasing the computational time of the MF training process.
Large Language Model Meets Graph Neural Network in Knowledge Distillation
Despite recent community revelations about the advancements and potential applications of Large Language Models (LLMs) in understanding Text-Attributed Graph (TAG), the deployment of LLMs for production is hindered by its high computational and storage requirements, as well as long latencies during model inference.
Structure-reinforced Transformer for Dynamic Graph Representation Learning with Edge Temporal States
The burgeoning field of dynamic graph representation learning, fuelled by the increasing demand for graph data analysis in real-world applications, poses both enticing opportunities and formidable challenges.
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