Search Results for author:
Found 2 papers, 1 papers with code
SPT: Fine-Tuning Transformer-based Language Models Efficiently with Sparsification
Thus, we design the sparse MHA module, which computes and stores only large attention weights to reduce memory consumption, and the routed FFN module, which dynamically activates a subset of model parameters for each token to reduce computation cost.
Vertex-Centric Visual Programming for Graph Neural Networks
Graph neural networks (GNNs) have achieved remarkable performance in many graph analytics tasks such as node classification, link prediction and graph clustering.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
