NIPS Proceedingsβ

Maneesh Sahani

23 Papers

  • Flexible and accurate inference and learning for deep generative models (2018)
  • Temporal alignment and latent Gaussian process factor inference in population spike trains (2018)
  • Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) (2015)
  • Extracting regions of interest from biological images with convolutional sparse block coding (2013)
  • Recurrent linear models of simultaneously-recorded neural populations (2013)
  • Learning visual motion in recurrent neural networks (2012)
  • Spectral learning of linear dynamics from generalised-linear observations with application to neural population data (2012)
  • Dynamical segmentation of single trials from population neural data (2011)
  • Empirical models of spiking in neural populations (2011)
  • Probabilistic amplitude and frequency demodulation (2011)
  • Occlusive Components Analysis (2009)
  • Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity (2008)
  • Inferring Elapsed Time from Stochastic Neural Processes (2007)
  • Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes (2007)
  • Modeling Natural Sounds with Modulation Cascade Processes (2007)
  • On Sparsity and Overcompleteness in Image Models (2007)
  • Extracting Dynamical Structure Embedded in Neural Activity (2005)
  • A Biologically Plausible Algorithm for Reinforcement-shaped Representational Learning (2003)
  • Reconstructing MEG Sources with Unknown Correlations (2003)
  • Adaptation and Unsupervised Learning (2002)
  • Evidence Optimization Techniques for Estimating Stimulus-Response Functions (2002)
  • How Linear are Auditory Cortical Responses? (2002)
  • On the Separation of Signals from Neighboring Cells in Tetrode Recordings (1997)