Search Results for author: David E. Carlson

Found 12 papers, 2 papers with code

On Target Shift in Adversarial Domain Adaptation

no code implementations15 Mar 2019 Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson

In this work, we propose a method called Domain Adversarial nets for Target Shift (DATS) to address label shift while learning a domain invariant representation.

Domain Adaptation

Extracting Relationships by Multi-Domain Matching

1 code implementation NeurIPS 2018 Yitong Li, Michael Murias, Geraldine Dawson, David E. Carlson

This methodology builds on existing distribution-matching approaches by assuming that source domains are varied and outcomes multi-factorial.

Domain Adaptation Time Series +2

Cross-Spectral Factor Analysis

no code implementations NeurIPS 2017 Neil Gallagher, Kyle R. Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, David E. Carlson

To facilitate understanding of network-level synchronization between brain regions, we introduce a novel model of multisite low-frequency neural recordings, such as local field potentials (LFPs) and electroencephalograms (EEGs).

Targeting EEG/LFP Synchrony with Neural Nets

no code implementations NeurIPS 2017 Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, Kafui Dzirasa, Lawrence Carin, David E. Carlson

We consider the analysis of Electroencephalography (EEG) and Local Field Potential (LFP) datasets, which are “big” in terms of the size of recorded data but rarely have sufficient labels required to train complex models (e. g., conventional deep learning methods).

EEG Electroencephalogram (EEG)

GP Kernels for Cross-Spectrum Analysis

no code implementations NeurIPS 2015 Kyle R. Ulrich, David E. Carlson, Kafui Dzirasa, Lawrence Carin

An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels.

Gaussian Processes Time Series +1

Preconditioned Spectral Descent for Deep Learning

no code implementations NeurIPS 2015 David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher

These challenges include, but are not limited to, the non-convexity of learning objectives and estimating the quantities needed for optimization algorithms, such as gradients.

On the relations of LFPs & Neural Spike Trains

no code implementations NeurIPS 2014 David E. Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin

One of the goals of neuroscience is to identify neural networks that correlate with important behaviors, environments, or genotypes.

Clustering Dictionary Learning

Analysis of Brain States from Multi-Region LFP Time-Series

no code implementations NeurIPS 2014 Kyle R. Ulrich, David E. Carlson, Wenzhao Lian, Jana S. Borg, Kafui Dzirasa, Lawrence Carin

The LFPs are modeled as a mixture of GPs, with state- and region-dependent mixture weights, and with the spectral content of the data encoded in GP spectral mixture covariance kernels.

Gaussian Processes Time Series +1

Real-Time Inference for a Gamma Process Model of Neural Spiking

no code implementations NeurIPS 2013 David E. Carlson, Vinayak Rao, Joshua T. Vogelstein, Lawrence Carin

With simultaneous measurements from ever increasing populations of neurons, there is a growing need for sophisticated tools to recover signals from individual neurons.

On the Analysis of Multi-Channel Neural Spike Data

no code implementations NeurIPS 2011 Bo Chen, David E. Carlson, Lawrence Carin

Nonparametric Bayesian methods are developed for analysis of multi-channel spike-train data, with the feature learning and spike sorting performed jointly.

Dictionary Learning Spike Sorting

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