14 citations · 45 across the 10 of their papers we have counts for
11 papers
A Closed-Form Solution for Kernel Adaptive Filtering
Benjamin Colburn, Luis G. Sanchez Giraldo, Kan Li +1
Unlike the conventional kernel adaptive filtering (KAF) approach of using a fixed kernel to define the Reproducing Kernel Hilbert Space (RKHS), this paper embeds the statistics of…
The Representation Jensen-Shannon Divergence
Jhoan K. Hoyos-Osorio, Luis G. Sanchez-Giraldo
Quantifying the difference between probability distributions is crucial in machine learning. However, estimating statistical divergences from empirical samples is challenging due t…
DiME: Maximizing Mutual Information by a Difference of Matrix-Based Entropies
Oscar Skean, Jhoan Keider Hoyos Osorio, Austin J. Brockmeier +1
We introduce an information-theoretic quantity with similar properties to mutual information that can be estimated from data without making explicit assumptions on the underlying d…
The Representation Jensen-Rényi Divergence
Jhoan Keider Hoyos Osorio, Oscar Skean, Austin J. Brockmeier +1
We introduce a divergence measure between data distributions based on operators in reproducing kernel Hilbert spaces defined by kernels. The empirical estimator of the divergence i…
Inference via Sparse Coding in a Hierarchical Vision Model
Joshua Bowren, Luis Sanchez-Giraldo, Odelia Schwartz
Sparse coding has been incorporated in models of the visual cortex for its computational advantages and connection to biology. But how the level of sparsity contributes to performa…
Multiscale Principle of Relevant Information for Hyperspectral Image Classification
Yantao Wei, Shujian Yu, Luis Sanchez Giraldo +1
This paper proposes a novel architecture, termed multiscale principle of relevant information (MPRI), to learn discriminative spectral-spatial features for hyperspectral image (HSI…
Multivariate Extension of Matrix-based Renyi's α-order Entropy Functional
Shujian Yu, Luis Gonzalo Sanchez Giraldo, Robert Jenssen +1
The matrix-based Renyi's α-order entropy functional was recently introduced using the normalized eigenspectrum of a Hermitian matrix of the projected data in a reproducing kernel H…
Correspondence of Deep Neural Networks and the Brain for Visual Textures
Md Nasir Uddin Laskar, Luis G Sanchez Giraldo, Odelia Schwartz
Deep convolutional neural networks (CNNs) trained on objects and scenes have shown intriguing ability to predict some response properties of visual cortical neurons. However, the f…
Integrating Flexible Normalization into Mid-Level Representations of Deep Convolutional Neural Networks
Luis Gonzalo Sanchez Giraldo, Odelia Schwartz
Deep convolutional neural networks (CNNs) are becoming increasingly popular models to predict neural responses in visual cortex. However, contextual effects, which are prevalent in…
Rate-Distortion Auto-Encoders
Luis G. Sanchez Giraldo, Jose C. Principe
A rekindled the interest in auto-encoder algorithms has been spurred by recent work on deep learning. Current efforts have been directed towards effective training of auto-encoder…
Information Theoretic Learning with Infinitely Divisible Kernels
Luis G. Sanchez Giraldo, Jose C. Principe
In this paper, we develop a framework for information theoretic learning based on infinitely divisible matrices. We formulate an entropy-like functional on positive definite matric…