This graph conversion may create excessive replicas and result in very large graphs, causing difficulties in workload balancing. A few tools have been developed for hypergraph partitioning, but they are not general-purpose frameworks for hypergraph processing. In this paper, we propose HyperX, a ...
This graph conversion may create excessive replicas and result in very large graphs, causing difficulties in workload balancing. A few tools have been developed for hypergraph partitioning, but they are not general-purpose frameworks for hypergraph processing. In this paper, we propose HyperX, a ...
Recently, machine learning methods, including reservoir computing (RC), have been tremendously successful in predicting complex dynamics in many fields. However, a present challenge lies in pushing for the limit of prediction accuracy while maintaining t
Q: How can Cleora be so fast and accurate at the same time? A: Not using negative sampling is a great boon. By constructing the (sparse) Markov transition matrix, Cleora explicitly performs all possible random walks in a hypergraph in one big step (a single matrix multiplication). That's...
ensuring sufficient memory resources to keep the experiment running efficiently when processing large amounts of data.In this experiment, we used the following hyperparameter configurations: Learning Rate: We chose 0.01 or the default 0.001 as the learning rate, depending on the specific dataset.Bat...
In today’s information explosion era, the number of various types of information increases exponentially, and image datasets become vast and diverse, making fast image retrieval challenging [1–3]. Show abstract Optimal Transport Quantization Based on Cross-X Semantic Hypergraph Learning for Fine-grai...