780 citations · 1.8k across the 76 of their papers we have counts for
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Local power estimation of neuromodulations using point process modeling
Shailaja Akella, Ali Mohebi, Kiersten Riels +3
Extracellular electrical potentials (EEP) recorded from the brain are an active manifestation of all cellular processes that propagate within a volume of brain tissue. A standard a…
Unsupervised Foveal Vision Neural Networks with Top-Down Attention
Ryan Burt, Nina N. Thigpen, Andreas Keil +1
Deep learning architectures are an extremely powerful tool for recognizing and classifying images. However, they require supervised learning and normally work on vectors the size o…
PRI-VAE: Principle-of-Relevant-Information Variational Autoencoders
Yanjun Li, Shujian Yu, Jose C. Principe +2
Although substantial efforts have been made to learn disentangled representations under the variational autoencoder (VAE) framework, the fundamental properties to the dynamics of l…
Interpretable Fault Detection using Projections of Mutual Information Matrix
Feiya Lv, Shujian Yu, Chenglin Wen +1
This paper presents a novel mutual information (MI) matrix based method for fault detection. Given a -dimensional fault process, the MI matrix is a matrix in which…
Modularizing Deep Learning via Pairwise Learning With Kernels
Shiyu Duan, Shujian Yu, Jose Principe
By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces,…
Towards a Kernel based Uncertainty Decomposition Framework for Data and Models
Rishabh Singh, Jose C. Principe
This paper introduces a new framework for quantifying predictive uncertainty for both data and models that relies on projecting the data into a Gaussian reproducing kernel Hilbert…
Fast Estimation of Information Theoretic Learning Descriptors using Explicit Inner Product Spaces
Kan Li, Jose C. Principe
Kernel methods form a theoretically-grounded, powerful and versatile framework to solve nonlinear problems in signal processing and machine learning. The standard approach relies o…