NIPS Proceedingsβ

John P. Cunningham

14 Papers

  • BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos (2019)
  • Deep Random Splines for Point Process Intensity Estimation of Neural Population Data (2019)
  • Paraphrase Generation with Latent Bag of Words (2019)
  • The continuous Bernoulli: fixing a pervasive error in variational autoencoders (2019)
  • Automated scalable segmentation of neurons from multispectral images (2016)
  • Linear dynamical neural population models through nonlinear embeddings (2016)
  • Bayesian Active Model Selection with an Application to Automated Audiometry (2015)
  • High-dimensional neural spike train analysis with generalized count linear dynamical systems (2015)
  • Clustered factor analysis of multineuronal spike data (2014)
  • Fast Kernel Learning for Multidimensional Pattern Extrapolation (2014)
  • Dynamical segmentation of single trials from population neural data (2011)
  • Empirical models of spiking in neural populations (2011)
  • Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity (2008)
  • Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes (2007)