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Found 13 papers, 3 papers with code
Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation
A registration-based data augmentation network creates realistic, labeled samples, while a feature distillation module helps the student network learn segmentation from these samples, guided by the teacher network.
On Target Shift in Adversarial Domain Adaptation
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.
Extracting Relationships by Multi-Domain Matching
This methodology builds on existing distribution-matching approaches by assuming that source domains are varied and outcomes multi-factorial.
YASS: Yet Another Spike Sorter
Spike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data.
Cross-Spectral Factor Analysis
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
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).
Preconditioned Spectral Descent for Deep Learning
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.
GP Kernels for Cross-Spectrum Analysis
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.
Analysis of Brain States from Multi-Region LFP Time-Series
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.
On the relations of LFPs & Neural Spike Trains
One of the goals of neuroscience is to identify neural networks that correlate with important behaviors, environments, or genotypes.
Designed Measurements for Vector Count Data
We consider design of linear projection measurements for a vector Poisson signal model.
Real-Time Inference for a Gamma Process Model of Neural Spiking
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
Nonparametric Bayesian methods are developed for analysis of multi-channel spike-train data, with the feature learning and spike sorting performed jointly.
