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Incorporating Taylor Series and Recursive Structure in Neural Networks for Time Series Prediction
Time series analysis is relevant in various disciplines such as physics, biology, chemistry, and finance.
Exploring higher-order neural network node interactions with total correlation
In domains such as ecological systems, collaborations, and the human brain the variables interact in complex ways.
Compressed Diffusion
Diffusion maps are a commonly used kernel-based method for manifold learning, which can reveal intrinsic structures in data and embed them in low dimensions.
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
In order to model the dynamics of such systems given snapshot data, or local transitions, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN.
Multivariate f-divergence Estimation With Confidence
The problem of f-divergence estimation is important in the fields of machine learning, information theory, and statistics.
