14 citations · 54 across the 32 of their papers we have counts for
5 papers · 1 filter
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series
Audrey Der, Chin-Chia Michael Yeh, Renjie Wu +6
The most useful data mining primitives are distance measures. With an effective distance measure, it is possible to perform classification, clustering, anomaly detection, segmentat…
Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces
Prince O Aboagye, Yan Zheng, Michael Yeh +6
Optimal Transport (OT) provides a useful geometric framework to estimate the permutation matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the alignmen…
Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph
Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng +6
Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer. When applying…
One-Shot Learning on Attributed Sequences
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2
One-shot learning has become an important research topic in the last decade with many real-world applications. The goal of one-shot learning is to classify unlabeled instances when…
Deep Learning on Attributed Sequences
Zhongfang Zhuang
Recent research in feature learning has been extended to sequence data, where each instance consists of a sequence of heterogeneous items with a variable length. However, in many r…