NIPS Proceedings
^{β}
Books
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)