4 citations · 9 across the 7 of their papers we have counts for
8 papers
Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression
Taehyung Kwon, Yeonje Choi, Yeongho Kim +1
Spatio-temporal time series are widely used in real-world applications, including traffic prediction and weather forecasting. They are sequences of observations over extensive peri…
Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts
Jongha Lee, Taehyung Kwon, Heechan Moon +1
The problem of predicting node properties (e.g., node classes) in graphs has received significant attention due to its broad range of applications. Graphs from real-world datasets…
TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions
Taehyung Kwon, Jihoon Ko, Jinhong Jung +1
Many real-world datasets are represented as tensors, i.e., multi-dimensional arrays of numerical values. Storing them without compression often requires substantial space, which gr…
NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors
Taehyung Kwon, Jihoon Ko, Jinhong Jung +1
Many real-world data are naturally represented as a sparse reorderable matrix, whose rows and columns can be arbitrarily ordered (e.g., the adjacency matrix of a bipartite graph).…
BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning
Jihoon Ko, Shinhwan Kang, Taehyung Kwon +2
Continual Learning (CL) is the process of learning ceaselessly a sequence of tasks. Most existing CL methods deal with independent data (e.g., images and text) for which many bench…
Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs
Hyeonjeong Shin, Taehyung Kwon, Neil Shah +1
A variety of tasks on dynamic graphs, including anomaly detection, community detection, compression, and graph understanding, have been formulated as problems of identifying consti…
Learning to Pool in Graph Neural Networks for Extrapolation
Jihoon Ko, Taehyung Kwon, Kijung Shin +1
Graph neural networks (GNNs) are one of the most popular approaches to using deep learning on graph-structured data, and they have shown state-of-the-art performances on a variety…
SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams
Taehyung Kwon, Inkyu Park, Dongjin Lee +1
Consider traffic data (i.e., triplets in the form of source-destination-timestamp) that grow over time. Tensors (i.e., multi-dimensional arrays) with a time mode are widely used fo…