Graph sparsification via meta-learning
WebJun 10, 2024 · Graph sparsification concerns data reduction where an edge-reduced graph of a similar structure is preferred. Existing methods are mostly sampling-based, which introduce high computation complexity in general and lack of flexibility for a different reduction objective. We present SparRL, the first general and effective reinforcement … WebNov 1, 2024 · A Performance-Guided Graph Sparsification Approach to Scalable and Robust SPICE-Accurate Integrated Circuit Simulations. Article. Oct 2015. IEEE T COMPUT AID D. Xueqian Zhao. Lengfei Han. Zhuo Feng.
Graph sparsification via meta-learning
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WebJan 7, 2024 · MGAE has two core designs. First, we find that masking a high ratio of the input graph structure, e.g., $70\%$, yields a nontrivial and meaningful self-supervisory task that benefits downstream ... WebUnder the NeuralSparse framework, supervised graph sparsification could seamlessly connect with existing graph neural networks for more robust performance. Experimental results on both benchmark and private datasets show that NeuralSparse can yield up to 7.2% improvement in testing accuracy when working with existing graph neural networks …
WebWe present a novel edge sparsification approach for semi-supervised learning on undirected and attributed graphs. The main challenge is to retain few edges while … WebBi-level Meta-learning for Few-shot Domain Generalization Xiaorong Qin · Xinhang Song · Shuqiang Jiang Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information Weijie Su · Xizhou Zhu · Chenxin Tao · Lewei Lu · Bin Li · Gao Huang · Yu Qiao · Xiaogang Wang · Jie Zhou · Jifeng Dai
WebBi-level Meta-learning for Few-shot Domain Generalization Xiaorong Qin · Xinhang Song · Shuqiang Jiang Towards All-in-one Pre-training via Maximizing Multi-modal Mutual … Webpropose to use meta-learning to reduce the number of edges in the graph, concentrating on node classification task in semi-supervised setting. Essentially, by treating the graph …
WebDec 2, 2024 · The interconnectedness and interdependence of modern graphs are growing ever more complex, causing enormous resources for processing, storage, …
WebSparRL: Graph Sparsification via Deep Reinforcement Learning: MDP: Paper: Code: 2024: ACM TOIS: RioGNN: Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks: MDP: ... Meta-learning based spatial-temporal graph attention network for traffic signal control: DQN: Paper \ 2024: bit stuffing and unstuffing in cWebMay 3, 2024 · Effective Sparsification of Neural Networks with Global Sparsity Constraint. Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments. However, since magnitudes and relative importance of weights are very different for different layers of a neural network, existing ... bit stuffing and byte stuffing differenceWebJun 14, 2024 · Prevailing methods for graphs require abundant label and edge information for learning. When data for a new task are scarce, meta-learning can learn from prior … datasecurity plus crackWebJie Chen, Tengfei Ma, and Cao Xiao. 2024. Fastgcn: fast learning with graph convolutional networks via importance sampling. In ICLR. Google Scholar; Patrick L Combettes and Jean-Christophe Pesquet. 2011. Proximal splitting methods in signal processing. In Fixed-point algorithms for inverse problems in science and engineering. Springer, 185--212. bit stuffing c++WebMar 8, 2024 · A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening. arXiv preprint arXiv:1902.09702 (2024). ... Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang. 2024. Robust Graph Representation Learning via Neural Sparsification. In ICML . Google Scholar; Jie Zhou, Ganqu Cui, Zhengyan … datasecurity plus downloadWebJun 11, 2024 · Daniel A. Spielman and Shang-Hua Teng. 2011. Spectral Sparsification of Graphs. SIAM J. Comput. 40, 4 (2011), 981--1025. Google Scholar Digital Library; Hado … bit stuffing c codeWebUnder the NeuralSparse framework, supervised graph sparsification could seamlessly connect with existing graph neural networks for more robust performance. Experimental … bit stuck in makita impact driver