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Found 10 papers, 8 papers with code
TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions
While many tensor compression algorithms are available, many of them rely on strong data assumptions regarding its order, sparsity, rank, and smoothness.
NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors
The updates take time linear in the number of non-zeros in the input matrix, and the approximation of each entry can be retrieved in logarithmic time.
BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning
Compared to them, however, CL methods for graph data (graph CL) are relatively underexplored because of (a) the lack of standard experimental settings, especially regarding how to deal with the dependency between instances, (b) the lack of benchmark datasets and scenarios, and (c) high complexity in implementation and evaluation due to the dependency.
Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data
Recently, many deep-learning techniques have been applied to various weather-related prediction tasks, including precipitation nowcasting (i. e., predicting precipitation levels and locations in the near future).
Effective Training Strategies for Deep-learning-based Precipitation Nowcasting and Estimation
It is highlighted that our pre-training scheme and new loss function improve the critical success index (CSI) of nowcasting of heavy rainfall (at least 10 mm/hr) by up to 95. 7% and 43. 6%, respectively, at a 5-hr lead time.
Learning to Pool in Graph Neural Networks for Extrapolation
However, according to a recent study, a careful choice of pooling functions, which are used for the aggregation and readout operations in GNNs, is crucial for enabling GNNs to extrapolate.
Evolution of Real-world Hypergraphs: Patterns and Models without Oracles
What kind of macroscopic structural and dynamical patterns can we observe in real-world hypergraphs?
Social and Information Networks
SSumM: Sparse Summarization of Massive Graphs
SSumM not only merges nodes together but also sparsifies the summary graph, and the two strategies are carefully balanced based on the minimum description length principle.
Databases Social and Information Networks H.2.8
Hypergraph Motifs: Concepts, Algorithms, and Discoveries
(Q3) how can we identify domains which hypergraphs are from?
Social and Information Networks Databases Data Structures and Algorithms H.2.8
MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks
In this work, we present an inductive machine learning method, called Monte Carlo Simulator (MONSTOR), for estimating the influence of given seed nodes in social networks unseen during training.
