Density Ratio Estimation

Gaussian Process Based Filtering for Neural DecodingKarthik Lakshmanan, Humphrey Hu, Arun VenkatramanApril 24, 2013Sony PicturesUniversity of Pittsburgh

http://cs.cmu.edu/~arunvenk/academics/neural/# K. Lakshmanan, H. Hu, and A. VenkatramanSetup & Motivation# K. Lakshmanan, H. Hu, and A. Venkatraman2Model non-linear observation mapping with Gaussian Processes (GPs)Need to use Unscented Kalman Filter (UKF)

However, this can be slow to evaluate

Proposed Method# K. Lakshmanan, H. Hu, and A. Venkatraman3Dimensionality Reduction

# K. Lakshmanan, H. Hu, and A. Venkatraman

Results & ConclusionImproved decoding & produced a higher fidelity generative (observation) model

Trajectory ReconstructionFinal Cursor Position Neural Reconstruction% Improvement of GP-UKF over KF (both non-dim-reduced)33.6%42.8%43.4%% Improvement of GP-UKF (w/PCA) over KF (non-dim-reduced)22.2%16.8%-% Improvement of GP-UKF (w/FA) over KF (non-dim-reduced)-0.80%-7.20%-(trained on 1/5 of training data)

# K. Lakshmanan, H. Hu, and A. Venkatraman