Search Results for author: Austin J. Brockmeier

Found 7 papers, 3 papers with code

DiME: Maximizing Mutual Information by a Difference of Matrix-Based Entropies

1 code implementation19 Jan 2023 Oscar Skean, Jhoan Keider Hoyos Osorio, Austin J. Brockmeier, Luis Gonzalo Sanchez Giraldo

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 distribution.

Disentanglement Mutual Information Estimation

The Representation Jensen-Rényi Divergence

1 code implementation2 Dec 2021 Jhoan Keider Hoyos Osorio, Oscar Skean, Austin J. Brockmeier, Luis Gonzalo Sanchez Giraldo

We introduce a divergence measure between data distributions based on operators in reproducing kernel Hilbert spaces defined by kernels.

Exploring latent networks in resting-state fMRI using voxel-to-voxel causal modeling feature selection

no code implementations15 Nov 2021 Hassan Baker, Austin J. Brockmeier

To reveal the functional networks among these voxels, we then apply independent component analysis (ICA) to model these voxels' signals as a mixture of latent sources each defining a functional network.

feature selection

Shift-invariant waveform learning on epileptic ECoG

no code implementations6 Aug 2021 Carlos H. Mendoza-Cardenas, Austin J. Brockmeier

Seizure detection algorithms must discriminate abnormal neuronal activity associated with a seizure from normal neural activity in a variety of conditions.

Seizure Detection Seizure prediction

Searching for waveforms on spatially-filtered epileptic ECoG

1 code implementation25 Mar 2021 Carlos H. Mendoza-Cardenas, Austin J. Brockmeier

Seizures are one of the defining symptoms in patients with epilepsy, and due to their unannounced occurrence, they can pose a severe risk for the individual that suffers it.

Seizure prediction

Max-sliced Bures Distance for Interpreting Discrepancies

no code implementations1 Jan 2021 Austin J. Brockmeier, Claudio Cesar Claros, Carlos H. Mendoza-Cardenas, Yüksel Karahan, Matthew S. Emigh, Luis Gonzalo Sanchez Giraldo

We propose the max-sliced Bures distance, a lower bound on the max-sliced Wasserstein-2 distance, to identify the instances associated with the maximum discrepancy between two samples.

Distributed Document and Phrase Co-embeddings for Descriptive Clustering

no code implementations EACL 2017 Motoki Sato, Austin J. Brockmeier, Georgios Kontonatsios, Tingting Mu, John Y. Goulermas, Jun{'}ichi Tsujii, Sophia Ananiadou

Descriptive document clustering aims to automatically discover groups of semantically related documents and to assign a meaningful label to characterise the content of each cluster.

Clustering Descriptive +2

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