Maneesh Sahani

Publication Activity (10 Years)

Years Active: 1997-2023
Publications (10 Years): 37

Top Topics

Generative Model

Minimum Description Length

Reinforcement Learning

Top Venues

Publications

William I. Walker, Hugo Soulat, Changmin Yu, Maneesh Sahani
Unsupervised representation learning with recognition-parametrised probabilistic models. AISTATS (2023)
Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matt M. Botvinick
Minimum Description Length Control. ICLR (2023)
Changmin Yu, Neil Burgess, Maneesh Sahani, Sam Gershman
Successor-Predecessor Intrinsic Exploration. CoRR (2023)
Changmin Yu, Neil Burgess, Maneesh Sahani, Samuel J. Gershman
Successor-Predecessor Intrinsic Exploration. NeurIPS (2023)
William I. Walker, Arthur Gretton, Maneesh Sahani
Prediction under Latent Subgroup Shifts with High-Dimensional Observations. CoRR (2023)
Ted Moskovitz, Samo Hromadka, Ahmed Touati, Diana Borsa, Maneesh Sahani
A State Representation for Diminishing Rewards. NeurIPS (2023)
Ted Moskovitz, Samo Hromadka, Ahmed Touati, Diana Borsa, Maneesh Sahani
A State Representation for Diminishing Rewards. CoRR (2023)
Ted Moskovitz, Spencer R. Wilson, Maneesh Sahani
A First-Occupancy Representation for Reinforcement Learning. ICLR (2022)
Mehrdad Salmasi, Maneesh Sahani
Learning neural codes for perceptual uncertainty. ISIT (2022)
Changmin Yu, Hugo Soulat, Neil Burgess, Maneesh Sahani
Amortised Inference in Structured Generative Models with Explaining Away. CoRR (2022)
William I. Walker, Hugo Soulat, Changmin Yu, Maneesh Sahani
Unsupervised representational learning with recognition-parametrised probabilistic models. CoRR (2022)
Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matthew M. Botvinick
Minimum Description Length Control. CoRR (2022)
Changmin Yu, Hugo Soulat, Neil Burgess, Maneesh Sahani
Structured Recognition for Generative Models with Explaining Away. NeurIPS (2022)
Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto
Deep learning, reinforcement learning, and world models. Neural Networks 152 (2022)
Ted Moskovitz, Spencer R. Wilson, Maneesh Sahani
A First-Occupancy Representation for Reinforcement Learning. CoRR (2021)
Hugo Soulat, Sepiedeh Keshavarzi, Troy W. Margrie, Maneesh Sahani
Probabilistic Tensor Decomposition of Neural Population Spiking Activity. NeurIPS (2021)
Grace W. Lindsay, Josh Merel, Tom Mrsic-Flogel, Maneesh Sahani
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning. CoRR (2021)
Li Kevin Wenliang, Theodore Moskovitz, Heishiro Kanagawa, Maneesh Sahani
Amortised Learning by Wake-Sleep. CoRR (2020)
Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo
Organizing recurrent network dynamics by task-computation to enable continual learning. NeurIPS (2020)
Li K. Wenliang, Theodore Moskovitz, Heishiro Kanagawa, Maneesh Sahani
Amortised Learning by Wake-Sleep. ICML (2020)
Virginia Rutten, Alberto Bernacchia, Maneesh Sahani, Guillaume Hennequin
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data. NeurIPS (2020)
Li Kevin Wenliang, Maneesh Sahani
A neurally plausible model for online recognition and postdiction in a dynamical environment. NeurIPS (2019)
Eszter Vértes, Maneesh Sahani
A neurally plausible model learns successor representations in partially observable environments. CoRR (2019)
Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani
Learning interpretable continuous-time models of latent stochastic dynamical systems. CoRR (2019)
Rahul Singh, Maneesh Sahani, Arthur Gretton
Kernel Instrumental Variable Regression. CoRR (2019)
Rahul Singh, Maneesh Sahani, Arthur Gretton
Kernel Instrumental Variable Regression. NeurIPS (2019)
Eszter Vértes, Maneesh Sahani
A neurally plausible model learns successor representations in partially observable environments. NeurIPS (2019)
Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani
Learning interpretable continuous-time models of latent stochastic dynamical systems. ICML (2019)
Eszter Vértes, Maneesh Sahani
Flexible and accurate inference and learning for deep generative models. CoRR (2018)
Lea Duncker, Maneesh Sahani
Temporal alignment and latent Gaussian process factor inference in population spike trains. NeurIPS (2018)
Gergo Bohner, Maneesh Sahani
Empirical fixed point bifurcation analysis. CoRR (2018)
Eszter Vértes, Maneesh Sahani
Flexible and accurate inference and learning for deep generative models. NeurIPS (2018)
Itay Lieder, Vincent Adam, Maneesh Sahani, Merav Ahissar
Modelling the dynamics of integrating context into perception: in good and in poor readers. CogSci (2017)
Laura Douglas, Iliyan Zarov, Konstantinos Gourgoulias, Chris Lucas, Chris Hart, Adam Baker, Maneesh Sahani, Yura Perov, Saurabh Johri
A Universal Marginalizer for Amortized Inference in Generative Models. CoRR (2017)
Vincent Adam, James Hensman, Maneesh Sahani
Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference. MLSP (2016)
Maneesh Sahani, Gergo Bohner, Arne Meyer
Score-matching estimators for continuous-time point-process regression models. MLSP (2016)
Gergo Bohner, Maneesh Sahani
Convolutional higher order matching pursuit. MLSP (2016)
Ross S. Williamson, Maneesh Sahani, Jonathan W. Pillow
The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction. PLoS Comput. Biol. 11 (4) (2015)
Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltán Szabó, Lars Buesing, Maneesh Sahani
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM). NIPS (2015)
Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke
Efficient occlusive components analysis. J. Mach. Learn. Res. 15 (1) (2014)
Richard E. Turner, Maneesh Sahani
Time-Frequency Analysis as Probabilistic Inference. IEEE Trans. Signal Process. 62 (23) (2014)
Marius Pachitariu, Maneesh Sahani
Regularization and nonlinearities for neural language models: when are they needed? CoRR (2013)
Marta I. Garrido, Maneesh Sahani, Raymond J. Dolan
Outlier Responses Reflect Sensitivity to Statistical Structure in the Human Brain. PLoS Comput. Biol. 9 (3) (2013)
Marius Pachitariu, Adam M. Packer, Noah Pettit, Henry Dalgleish, Michael Häusser, Maneesh Sahani
Extracting regions of interest from biological images with convolutional sparse block coding. NIPS (2013)
Marius Pachitariu, Biljana Petreska, Maneesh Sahani
Recurrent linear models of simultaneously-recorded neural populations. NIPS (2013)
Lars Buesing, Jakob H. Macke, Maneesh Sahani
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS (2012)
Gautham J. Mysore, Maneesh Sahani
Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separation. ICML (2012)
Richard E. Turner, Maneesh Sahani
Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference. ICASSP (2012)
Marius Pachitariu, Maneesh Sahani
Learning visual motion in recurrent neural networks. NIPS (2012)
Richard E. Turner, Maneesh Sahani
Probabilistic amplitude and frequency demodulation. NIPS (2011)
Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
Empirical models of spiking in neural populations. NIPS (2011)
Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
Dynamical segmentation of single trials from population neural data. NIPS (2011)
Richard E. Turner, Maneesh Sahani
Demodulation as Probabilistic Inference. IEEE ACM Trans. Audio Speech Lang. Process. 19 (8) (2011)
Richard E. Turner, Maneesh Sahani
Statistical inference for single- and multi-band Probabilistic Amplitude Demodulation. ICASSP (2010)
Pietro Berkes, Richard E. Turner, Maneesh Sahani
A Structured Model of Video Reproduces Primary Visual Cortical Organisation. PLoS Comput. Biol. 5 (9) (2009)
Jörg Lücke, Richard E. Turner, Maneesh Sahani, Marc Henniges
Occlusive Components Analysis. NIPS (2009)
John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
Fast Gaussian process methods for point process intensity estimation. ICML (2008)
Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. NIPS (2008)
Gopal Santhanam, Byron M. Yu, Vikash Gilja, Stephen I. Ryu, Afsheen Afshar, Maneesh Sahani, Krishna V. Shenoy
A factor-analysis decoder for high-performance neural prostheses. ICASSP (2008)
Jörg Lücke, Maneesh Sahani
Maximal Causes for Non-linear Component Extraction. J. Mach. Learn. Res. 9 (2008)
Simon J. D. Prince, Jania Aghajanian, Umar Mohammed, Maneesh Sahani
Latent Identity Variables: Biometric Matching Without Explicit Identity Estimation. ICB (2007)
Misha Ahrens, Maneesh Sahani
Inferring Elapsed Time from Stochastic Neural Processes. NIPS (2007)
Byron M. Yu, John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models. ICONIP (1) (2007)
Jörg Lücke, Maneesh Sahani
Generalized Softmax Networks for Non-linear Component Extraction. ICANN (1) (2007)
Richard E. Turner, Maneesh Sahani
A Maximum-Likelihood Interpretation for Slow Feature Analysis. Neural Comput. 19 (4) (2007)
Richard E. Turner, Maneesh Sahani
Modeling Natural Sounds with Modulation Cascade Processes. NIPS (2007)
John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes. NIPS (2007)
Pietro Berkes, Richard E. Turner, Maneesh Sahani
On Sparsity and Overcompleteness in Image Models. NIPS (2007)
Richard E. Turner, Maneesh Sahani
Probabilistic Amplitude Demodulation. ICA (2007)
Byron M. Yu, Afsheen Afshar, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
Extracting Dynamical Structure Embedded in Neural Activity. NIPS (2005)
Kensuke Sekihara, Maneesh Sahani, Srikantan S. Nagarajan
A simple nonparametric statistical thresholding for MEG spatial-filter source reconstruction images. NeuroImage 27 (2) (2005)
Kensuke Sekihara, Maneesh Sahani, Srikantan S. Nagarajan
Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction. NeuroImage 25 (4) (2005)
Maneesh Sahani, Peter Dayan
Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity. Neural Comput. 15 (10) (2003)
Maneesh Sahani, Srikantan S. Nagarajan
Reconstructing MEG Sources with Unknown Correlations. NIPS (2003)
Maneesh Sahani
A Biologically Plausible Algorithm for Reinforcement-shaped Representational Learning. NIPS (2003)
Maneesh Sahani, Jennifer F. Linden
Evidence Optimization Techniques for Estimating Stimulus-Response Functions. NIPS (2002)
Peter Dayan, Maneesh Sahani, Gregoire Deback
Adaptation and Unsupervised Learning. NIPS (2002)
Maneesh Sahani, Jennifer F. Linden
How Linear are Auditory Cortical Responses?. NIPS (2002)
John S. Pezaris, Maneesh Sahani, Richard A. Andersen
Spike train coherence in macaque parietal cortex during a memory saccade task. Neurocomputing (2000)
Michael Wehr, John S. Pezaris, Maneesh Sahani
Simultaneous paired intracellular and tetrode recordings for evaluating the performance of spike sorting algorithms. Neurocomputing (1999)
John S. Pezaris, Maneesh Sahani, Richard A. Andersen
Response-locked changes in auto- and cross-covariations in parietal cortex. Neurocomputing (1999)
Maneesh Sahani, John S. Pezaris, Richard A. Andersen
On the Separation of Signals from Neighboring Cells in Tetrode Recordings. NIPS (1997)