Maneesh Sahani

87 papers A* 19A 1B 2Misc 3Journal 34Unranked 28

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA. ICLR Changmin Yu, Maneesh Sahani, Máté Lengyel
2025	J	`jnl`	Maximum Likelihood Learning of Latent Dynamics Without Reconstruction. CoRR Samo Hromadka, Kai Biegun, Lior Fox, James Heald, Maneesh Sahani
2024	A*	`conf`	Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. NeurIPS Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
2024	J	`jnl`	Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. CoRR Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
2024	J	`jnl`	Understanding dual process cognition via the minimum description length principle. PLoS Comput. Biol. Ted Moskovitz, Kevin J. Miller, Maneesh Sahani, Matthew M. Botvinick
2023	A*	`conf`	A State Representation for Diminishing Rewards. NeurIPS Ted Moskovitz, Samo Hromadka, Ahmed Touati, Diana Borsa, Maneesh Sahani
2023	J	`jnl`	A State Representation for Diminishing Rewards. CoRR Ted Moskovitz, Samo Hromadka, Ahmed Touati, Diana Borsa, Maneesh Sahani
2023	A*	`conf`	Minimum Description Length Control. ICLR Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matt M. Botvinick
2023	J	`jnl`	Prediction under Latent Subgroup Shifts with High-Dimensional Observations. CoRR William I. Walker, Arthur Gretton, Maneesh Sahani
2023	A*	`conf`	Successor-Predecessor Intrinsic Exploration. NeurIPS Changmin Yu, Neil Burgess, Maneesh Sahani, Samuel J. Gershman
2023	J	`jnl`	Successor-Predecessor Intrinsic Exploration. CoRR Changmin Yu, Neil Burgess, Maneesh Sahani, Sam Gershman
2023	A	`conf`	Unsupervised representation learning with recognition-parametrised probabilistic models. AISTATS William I. Walker, Hugo Soulat, Changmin Yu, Maneesh Sahani
2022	A*	`conf`	A First-Occupancy Representation for Reinforcement Learning. ICLR Ted Moskovitz, Spencer R. Wilson, Maneesh Sahani
2022	J	`jnl`	Amortised Inference in Structured Generative Models with Explaining Away. CoRR Changmin Yu, Hugo Soulat, Neil Burgess, Maneesh Sahani
2022	J	`jnl`	Deep learning, reinforcement learning, and world models. Neural Networks Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto
2022	B	`conf`	Learning neural codes for perceptual uncertainty. ISIT Mehrdad Salmasi, Maneesh Sahani
2022	J	`jnl`	Minimum Description Length Control. CoRR Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matthew M. Botvinick
2022	A*	`conf`	Structured Recognition for Generative Models with Explaining Away. NeurIPS Changmin Yu, Hugo Soulat, Neil Burgess, Maneesh Sahani
2022	J	`jnl`	Unsupervised representational learning with recognition-parametrised probabilistic models. CoRR William I. Walker, Hugo Soulat, Changmin Yu, Maneesh Sahani
2021	J	`jnl`	A First-Occupancy Representation for Reinforcement Learning. CoRR Ted Moskovitz, Spencer R. Wilson, Maneesh Sahani
2021	J	`jnl`	Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning. CoRR Grace W. Lindsay, Josh Merel, Tom Mrsic-Flogel, Maneesh Sahani
2021	A*	`conf`	Probabilistic Tensor Decomposition of Neural Population Spiking Activity. NeurIPS Hugo Soulat, Sepiedeh Keshavarzi, Troy W. Margrie, Maneesh Sahani
2020	A*	`conf`	Amortised Learning by Wake-Sleep. ICML Li K. Wenliang, Theodore H. Moskovitz, Heishiro Kanagawa, Maneesh Sahani
2020	J	`jnl`	Amortised Learning by Wake-Sleep. CoRR Li Kevin Wenliang, Theodore H. Moskovitz, Heishiro Kanagawa, Maneesh Sahani
2020	A*	`conf`	Non-reversible Gaussian processes for identifying latent dynamical structure in neural data. NeurIPS Virginia Rutten, Alberto Bernacchia, Maneesh Sahani, Guillaume Hennequin
2020	A*	`conf`	Organizing recurrent network dynamics by task-computation to enable continual learning. NeurIPS Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo
2019	A*	`conf`	A neurally plausible model for online recognition and postdiction in a dynamical environment. NeurIPS Li Kevin Wenliang, Maneesh Sahani
2019	A*	`conf`	A neurally plausible model learns successor representations in partially observable environments. NeurIPS Eszter Vértes, Maneesh Sahani
2019	J	`jnl`	A neurally plausible model learns successor representations in partially observable environments. CoRR Eszter Vértes, Maneesh Sahani
2019	A*	`conf`	Kernel Instrumental Variable Regression. NeurIPS Rahul Singh, Maneesh Sahani, Arthur Gretton
2019	J	`jnl`	Kernel Instrumental Variable Regression. CoRR Rahul Singh, Maneesh Sahani, Arthur Gretton
2019	A*	`conf`	Learning interpretable continuous-time models of latent stochastic dynamical systems. ICML Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani
2019	J	`jnl`	Learning interpretable continuous-time models of latent stochastic dynamical systems. CoRR Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani
2018	J	`jnl`	Empirical fixed point bifurcation analysis. CoRR Gergo Bohner, Maneesh Sahani
2018	A*	`conf`	Flexible and accurate inference and learning for deep generative models. NeurIPS Eszter Vértes, Maneesh Sahani
2018	J	`jnl`	Flexible and accurate inference and learning for deep generative models. CoRR Eszter Vértes, Maneesh Sahani
2018	A*	`conf`	Temporal alignment and latent Gaussian process factor inference in population spike trains. NeurIPS Lea Duncker, Maneesh Sahani
2017	J	`jnl`	A Universal Marginalizer for Amortized Inference in Generative Models. CoRR Laura Douglas, Iliyan Zarov, Konstantinos Gourgoulias, Chris Lucas, Chris Hart, Adam Baker, Maneesh Sahani, Yura Perov, Saurabh Johri
2017	B	`conf`	Modelling the dynamics of integrating context into perception: in good and in poor readers. CogSci Itay Lieder, Vincent Adam, Maneesh Sahani, Merav Ahissar
2016	—	`conf`	Convolutional higher order matching pursuit. MLSP Gergo Bohner, Maneesh Sahani
2016	—	`conf`	Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference. MLSP Vincent Adam, James Hensman, Maneesh Sahani
2016	—	`conf`	Score-matching estimators for continuous-time point-process regression models. MLSP Maneesh Sahani, Gergo Bohner, Arne Meyer
2015	—	`conf`	Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM). NIPS Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltán Szabó, Lars Buesing, Maneesh Sahani
2015	J	`jnl`	The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction. PLoS Comput. Biol. Ross S. Williamson, Maneesh Sahani, Jonathan W. Pillow
2014	J	`jnl`	Efficient occlusive components analysis. J. Mach. Learn. Res. Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke
2014	J	`jnl`	Time-Frequency Analysis as Probabilistic Inference. IEEE Trans. Signal Process. Richard E. Turner, Maneesh Sahani
2013	—	`conf`	Extracting regions of interest from biological images with convolutional sparse block coding. NIPS Marius Pachitariu, Adam M. Packer, Noah Pettit, Henry Dalgleish, Michael Häusser, Maneesh Sahani
2013	J	`jnl`	Outlier Responses Reflect Sensitivity to Statistical Structure in the Human Brain. PLoS Comput. Biol. Marta I. Garrido, Maneesh Sahani, Raymond J. Dolan
2013	—	`conf`	Recurrent linear models of simultaneously-recorded neural populations. NIPS Marius Pachitariu, Biljana Petreska, Maneesh Sahani
2013	J	`jnl`	Regularization and nonlinearities for neural language models: when are they needed? CoRR Marius Pachitariu, Maneesh Sahani
2012	Misc	`conf`	Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference. ICASSP Richard E. Turner, Maneesh Sahani
2012	—	`conf`	Learning visual motion in recurrent neural networks. NIPS Marius Pachitariu, Maneesh Sahani
2012	—	`conf`	Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS Lars Buesing, Jakob H. Macke, Maneesh Sahani
2012	A*	`conf`	Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separation. ICML Gautham J. Mysore, Maneesh Sahani
2011	J	`jnl`	Demodulation as Probabilistic Inference. IEEE ACM Trans. Audio Speech Lang. Process. Richard E. Turner, Maneesh Sahani
2011	—	`conf`	Dynamical segmentation of single trials from population neural data. NIPS Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
2011	—	`conf`	Empirical models of spiking in neural populations. NIPS Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
2011	—	`conf`	Probabilistic amplitude and frequency demodulation. NIPS Richard E. Turner, Maneesh Sahani
2010	Misc	`conf`	Statistical inference for single- and multi-band Probabilistic Amplitude Demodulation. ICASSP Richard E. Turner, Maneesh Sahani
2009	J	`jnl`	A Structured Model of Video Reproduces Primary Visual Cortical Organisation. PLoS Comput. Biol. Pietro Berkes, Richard E. Turner, Maneesh Sahani
2009	—	`conf`	Occlusive Components Analysis. NIPS Jörg Lücke, Richard E. Turner, Maneesh Sahani, Marc Henniges
2008	Misc	`conf`	A factor-analysis decoder for high-performance neural prostheses. ICASSP Gopal Santhanam, Byron M. Yu, Vikash Gilja, Stephen I. Ryu, Afsheen Afshar, Maneesh Sahani, Krishna V. Shenoy
2008	A*	`conf`	Fast Gaussian process methods for point process intensity estimation. ICML John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
2008	—	`conf`	Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. NIPS Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
2008	J	`jnl`	Maximal Causes for Non-linear Component Extraction. J. Mach. Learn. Res. Jörg Lücke, Maneesh Sahani
2007	J	`jnl`	A Maximum-Likelihood Interpretation for Slow Feature Analysis. Neural Comput. Richard E. Turner, Maneesh Sahani
2007	—	`conf`	Generalized Softmax Networks for Non-linear Component Extraction. ICANN (1) Jörg Lücke, Maneesh Sahani
2007	—	`conf`	Inferring Elapsed Time from Stochastic Neural Processes. NIPS Misha B. Ahrens, Maneesh Sahani
2007	—	`conf`	Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes. NIPS John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
2007	—	`conf`	Latent Identity Variables: Biometric Matching Without Explicit Identity Estimation. ICB Simon J. D. Prince, Jania Aghajanian, Umar Mohammed, Maneesh Sahani
2007	—	`conf`	Modeling Natural Sounds with Modulation Cascade Processes. NIPS Richard E. Turner, Maneesh Sahani
2007	—	`conf`	Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models. ICONIP (1) Byron M. Yu, John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
2007	—	`conf`	On Sparsity and Overcompleteness in Image Models. NIPS Pietro Berkes, Richard E. Turner, Maneesh Sahani
2007	—	`conf`	Probabilistic Amplitude Demodulation. ICA Richard E. Turner, Maneesh Sahani
2005	J	`jnl`	A simple nonparametric statistical thresholding for MEG spatial-filter source reconstruction images. NeuroImage Kensuke Sekihara, Maneesh Sahani, Srikantan S. Nagarajan
2005	—	`conf`	Extracting Dynamical Structure Embedded in Neural Activity. NIPS Byron M. Yu, Afsheen Afshar, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
2005	J	`jnl`	Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction. NeuroImage Kensuke Sekihara, Maneesh Sahani, Srikantan S. Nagarajan
2003	—	`conf`	A Biologically Plausible Algorithm for Reinforcement-shaped Representational Learning. NIPS Maneesh Sahani
2003	J	`jnl`	Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity. Neural Comput. Maneesh Sahani, Peter Dayan
2003	—	`conf`	Reconstructing MEG Sources with Unknown Correlations. NIPS Maneesh Sahani, Srikantan S. Nagarajan
2002	—	`conf`	Adaptation and Unsupervised Learning. NIPS Peter Dayan, Maneesh Sahani, Gregoire Deback
2002	—	`conf`	Evidence Optimization Techniques for Estimating Stimulus-Response Functions. NIPS Maneesh Sahani, Jennifer F. Linden
2002	—	`conf`	How Linear are Auditory Cortical Responses?. NIPS Maneesh Sahani, Jennifer F. Linden
2000	J	`jnl`	Spike train coherence in macaque parietal cortex during a memory saccade task. Neurocomputing John S. Pezaris, Maneesh Sahani, Richard A. Andersen
1999	J	`jnl`	Response-locked changes in auto- and cross-covariations in parietal cortex. Neurocomputing John S. Pezaris, Maneesh Sahani, Richard A. Andersen
1999	J	`jnl`	Simultaneous paired intracellular and tetrode recordings for evaluating the performance of spike sorting algorithms. Neurocomputing Michael Wehr, John S. Pezaris, Maneesh Sahani
1997	—	`conf`	On the Separation of Signals from Neighboring Cells in Tetrode Recordings. NIPS Maneesh Sahani, John S. Pezaris, Richard A. Andersen