Learning-augmented k-means clustering International Conference on Learning Representations (2021) Google Scholar 40 E. Grigorescu, Y.-S. Lin, S. Silwal, M. Song, S. Zhou Learning-augmented algorithms for online linear and semidefinite programming Adv. Neural Inf. Process. Syst., 35 (2022), pp...
Confronting these challenges, II-LA-KM, a learning-augmented clustering algorithm with improved initialization for rock discontinuity grouping, is proposed. Our method begins with heuristically selecting initial centers to ensure they are well-separated. Then, optimal transport is used to adjust these ...
Redundancy-free self-supervised relational learning for graph clustering. IEEE Trans Neural Netw Learn Syst, 2024. doi: https://doi.org/10.1109/TNNLS.2023.3314451 Wang Y G, Li M, Ma Z, et al. Haar graph pooling. In: Proceedings of the International Conference on Machine Learning, 2020. 995...
Cluster-based methods [17,34] leverage graph clustering to coarsen the input graph. In our framework, we reutilize this idea and generate a cluster assignment matrix S ∈ R|V|× ⌈ ρ |V|⌉S ∈ R|V|× ⌈ ρ |V|⌉, where each row corresponds to one node while each column ...
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It can be applied to clustering and hash learning to achieve the state-of-the-art results. This is the work performed while Weihua Hu was interning at Preferred Networks. Requirements You must have the following already installed on your system. Python 2.7 Chainer 1.21.0, sklearn, munkres ...
It indi- cates that task alignment is capable of mining the latent clustering knowledge to learn more personalized node characteristics.】 六,参考文献 【11,44,60,63,55,10,52,62,56】GraphSAINT 每个Task包括属于该聚类所有用户的交互 元更新部分不是在所有的testing task的loss和上进行更新,而是直接在...
Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes clustering and hash learning as special cases. Deep neural networks are promising to be used because they can model the non-line...
The Larson–Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high-temperature applications; however, poor generalizability and dependence on the constant C often result in sub-optimal pe
process, which automatically estimate the dictionary size and make no explicit assumption on the noise variance, while the drawback of them is the computational load. However, little attention in the literature has been paid to making the generalized clustering ability of the dictionary more stable....