41 citations · 203 across the 38 of their papers we have counts for
4 papers · 1 filter
Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck
Thanh T. Nguyen, Jaesik Choi
Information Bottleneck (IB) is a generalization of rate-distortion theory that naturally incorporates compression and relevance trade-offs for learning. Though the original IB has…
Grouped Convolutional Neural Networks for Multivariate Time Series
Subin Yi, Janghoon Ju, Man-Ki Yoon +1
Analyzing multivariate time series data is important for many applications such as automated control, fault diagnosis and anomaly detection. One of the key challenges is to learn l…
Discovering Latent Covariance Structures for Multiple Time Series
Anh Tong, Jaesik Choi
Analyzing multivariate time series data is important to predict future events and changes of complex systems in finance, manufacturing, and administrative decisions. The expressive…
Make Hawkes Processes Explainable by Decomposing Self-Triggering Kernels
Rafael Lima, Jaesik Choi
Hawkes Processes capture self-excitation and mutual-excitation between events when the arrival of an event makes future events more likely to happen. Identification of such tempora…