4 citations · 6 across the 2 of their papers we have counts for
5 papers
Convolution-Free Waveform Transformers for Multi-Lead ECG Classification
Annamalai Natarajan, Gregory Boverman, Yale Chang +2
We present our entry to the 2021 PhysioNet/CinC challenge - a waveform transformer model to detect cardiac abnormalities from ECG recordings. We compare the performance of the wave…
Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series
Asif Rahman, Yale Chang, Jonathan Rubin
Time series models with recurrent neural networks (RNNs) can have high accuracy but are unfortunately difficult to interpret as a result of feature-interactions, temporal-interacti…
Solving Interpretable Kernel Dimension Reduction
Chieh Wu, Jared Miller, Yale Chang +2
Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of the original data by optimizing kernel dependency measures that are capable of capturing n…
Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion
Chieh Wu, Jared Miller, Yale Chang +2
The Hilbert Schmidt Independence Criterion (HSIC) is a kernel dependence measure that has applications in various aspects of machine learning. Conveniently, the objectives of diffe…
Deep Kernel Learning for Clustering
Chieh Wu, Zulqarnain Khan, Yale Chang +2
We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data. Our neural network produces sample embeddings that are motivated by--…