7 citations · 10 across the 3 of their papers we have counts for
6 papers · 1 filter
Representer Point Selection for Explaining Deep Neural Networks
Chih-Kuan Yeh, Joon Sik Kim, Ian E. H. Yen +1
We propose to explain the predictions of a deep neural network, by pointing to the set of what we call representer points in the training set, for a given test point prediction. Sp…
Word Mover's Embedding: From Word2Vec to Document Embedding
Lingfei Wu, Ian E. H. Yen, Kun Xu +5
While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised…
Efficient Tensor Decomposition with Boolean Factors
Sung-En Chang, Xun Zheng, Ian E. H. Yen +2
Tensor decomposition has been extensively used as a tool for exploratory analysis. Motivated by neuroscience applications, we study tensor decomposition with Boolean factors. The r…
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability
Lingfei Wu, Ian E. H. Yen, Jie Chen +1
Kernel method has been developed as one of the standard approaches for nonlinear learning, which however, does not scale to large data set due to its quadratic complexity in the nu…
Random Warping Series: A Random Features Method for Time-Series Embedding
Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi +3
Time series data analytics has been a problem of substantial interests for decades, and Dynamic Time Warping (DTW) has been the most widely adopted technique to measure dissimilari…
Scalable Spectral Clustering Using Random Binning Features
Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen +3
Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems d…