default search action
Maneesh Sahani
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
- [j16]Ted Moskovitz, Kevin J. Miller, Maneesh Sahani, Matthew M. Botvinick:
Understanding dual process cognition via the minimum description length principle. PLoS Comput. Biol. 20(10): 1012383 (2024) - 2023
- [c51]William I. Walker, Hugo Soulat, Changmin Yu, Maneesh Sahani:
Unsupervised representation learning with recognition-parametrised probabilistic models. AISTATS 2023: 4209-4230 - [c50]Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matt M. Botvinick:
Minimum Description Length Control. ICLR 2023 - [c49]Ted Moskovitz, Samo Hromadka, Ahmed Touati, Diana Borsa, Maneesh Sahani:
A State Representation for Diminishing Rewards. NeurIPS 2023 - [c48]Changmin Yu, Neil Burgess, Maneesh Sahani, Samuel J. Gershman:
Successor-Predecessor Intrinsic Exploration. NeurIPS 2023 - [i16]Changmin Yu, Neil Burgess, Maneesh Sahani, Sam Gershman:
Successor-Predecessor Intrinsic Exploration. CoRR abs/2305.15277 (2023) - [i15]William I. Walker, Arthur Gretton, Maneesh Sahani:
Prediction under Latent Subgroup Shifts with High-Dimensional Observations. CoRR abs/2306.13472 (2023) - [i14]Ted Moskovitz, Samo Hromadka, Ahmed Touati, Diana Borsa, Maneesh Sahani:
A State Representation for Diminishing Rewards. CoRR abs/2309.03710 (2023) - 2022
- [j15]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: 267-275 (2022) - [c47]Ted Moskovitz, Spencer R. Wilson, Maneesh Sahani:
A First-Occupancy Representation for Reinforcement Learning. ICLR 2022 - [c46]Mehrdad Salmasi, Maneesh Sahani:
Learning neural codes for perceptual uncertainty. ISIT 2022: 2463-2468 - [c45]Changmin Yu, Hugo Soulat, Neil Burgess, Maneesh Sahani:
Structured Recognition for Generative Models with Explaining Away. NeurIPS 2022 - [i13]Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matthew M. Botvinick:
Minimum Description Length Control. CoRR abs/2207.08258 (2022) - [i12]Changmin Yu, Hugo Soulat, Neil Burgess, Maneesh Sahani:
Amortised Inference in Structured Generative Models with Explaining Away. CoRR abs/2209.05212 (2022) - [i11]William I. Walker, Hugo Soulat, Changmin Yu, Maneesh Sahani:
Unsupervised representational learning with recognition-parametrised probabilistic models. CoRR abs/2209.05661 (2022) - 2021
- [c44]Hugo Soulat, Sepiedeh Keshavarzi, Troy W. Margrie, Maneesh Sahani:
Probabilistic Tensor Decomposition of Neural Population Spiking Activity. NeurIPS 2021: 15969-15980 - [i10]Ted Moskovitz, Spencer R. Wilson, Maneesh Sahani:
A First-Occupancy Representation for Reinforcement Learning. CoRR abs/2109.13863 (2021) - [i9]Grace W. Lindsay, Josh Merel, Tom Mrsic-Flogel, Maneesh Sahani:
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning. CoRR abs/2112.02027 (2021) - 2020
- [c43]Li K. Wenliang, Theodore H. Moskovitz, Heishiro Kanagawa, Maneesh Sahani:
Amortised Learning by Wake-Sleep. ICML 2020: 10236-10247 - [c42]Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo:
Organizing recurrent network dynamics by task-computation to enable continual learning. NeurIPS 2020 - [c41]Virginia Rutten, Alberto Bernacchia, Maneesh Sahani, Guillaume Hennequin:
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data. NeurIPS 2020 - [i8]Li Kevin Wenliang, Theodore H. Moskovitz, Heishiro Kanagawa, Maneesh Sahani:
Amortised Learning by Wake-Sleep. CoRR abs/2002.09737 (2020)
2010 – 2019
- 2019
- [c40]Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani:
Learning interpretable continuous-time models of latent stochastic dynamical systems. ICML 2019: 1726-1734 - [c39]Rahul Singh, Maneesh Sahani, Arthur Gretton:
Kernel Instrumental Variable Regression. NeurIPS 2019: 4595-4607 - [c38]Li Kevin Wenliang, Maneesh Sahani:
A neurally plausible model for online recognition and postdiction in a dynamical environment. NeurIPS 2019: 9641-9652 - [c37]Eszter Vértes, Maneesh Sahani:
A neurally plausible model learns successor representations in partially observable environments. NeurIPS 2019: 13692-13702 - [i7]Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani:
Learning interpretable continuous-time models of latent stochastic dynamical systems. CoRR abs/1902.04420 (2019) - [i6]Rahul Singh, Maneesh Sahani, Arthur Gretton:
Kernel Instrumental Variable Regression. CoRR abs/1906.00232 (2019) - [i5]Eszter Vértes, Maneesh Sahani:
A neurally plausible model learns successor representations in partially observable environments. CoRR abs/1906.09480 (2019) - 2018
- [c36]Eszter Vértes, Maneesh Sahani:
Flexible and accurate inference and learning for deep generative models. NeurIPS 2018: 4170-4179 - [c35]Lea Duncker, Maneesh Sahani:
Temporal alignment and latent Gaussian process factor inference in population spike trains. NeurIPS 2018: 10466-10476 - [i4]Eszter Vértes, Maneesh Sahani:
Flexible and accurate inference and learning for deep generative models. CoRR abs/1805.11051 (2018) - [i3]Gergo Bohner, Maneesh Sahani:
Empirical fixed point bifurcation analysis. CoRR abs/1807.01486 (2018) - 2017
- [c34]Itay Lieder, Vincent Adam, Maneesh Sahani, Merav Ahissar:
Modelling the dynamics of integrating context into perception: in good and in poor readers. CogSci 2017 - [i2]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 abs/1711.00695 (2017) - 2016
- [c33]Vincent Adam, James Hensman, Maneesh Sahani:
Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference. MLSP 2016: 1-6 - [c32]Gergo Bohner, Maneesh Sahani:
Convolutional higher order matching pursuit. MLSP 2016: 1-6 - [c31]Maneesh Sahani, Gergo Bohner, Arne Meyer:
Score-matching estimators for continuous-time point-process regression models. MLSP 2016: 1-5 - 2015
- [j14]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) - [c30]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: 154-162 - 2014
- [j13]Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke:
Efficient occlusive components analysis. J. Mach. Learn. Res. 15(1): 2689-2722 (2014) - [j12]Richard E. Turner, Maneesh Sahani:
Time-Frequency Analysis as Probabilistic Inference. IEEE Trans. Signal Process. 62(23): 6171-6183 (2014) - 2013
- [j11]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) - [c29]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: 1745-1753 - [c28]Marius Pachitariu, Biljana Petreska, Maneesh Sahani:
Recurrent linear models of simultaneously-recorded neural populations. NIPS 2013: 3138-3146 - [i1]Marius Pachitariu, Maneesh Sahani:
Regularization and nonlinearities for neural language models: when are they needed? CoRR abs/1301.5650 (2013) - 2012
- [c27]Richard E. Turner, Maneesh Sahani:
Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference. ICASSP 2012: 2173-2176 - [c26]Gautham J. Mysore, Maneesh Sahani:
Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separation. ICML 2012 - [c25]Marius Pachitariu, Maneesh Sahani:
Learning visual motion in recurrent neural networks. NIPS 2012: 1331-1339 - [c24]Lars Buesing, Jakob H. Macke, Maneesh Sahani:
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 2012: 1691-1699 - 2011
- [j10]Richard E. Turner, Maneesh Sahani:
Demodulation as Probabilistic Inference. IEEE ACM Trans. Audio Speech Lang. Process. 19(8): 2398-2411 (2011) - [c23]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: 756-764 - [c22]Richard E. Turner, Maneesh Sahani:
Probabilistic amplitude and frequency demodulation. NIPS 2011: 981-989 - [c21]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: 1350-1358 - 2010
- [c20]Richard E. Turner, Maneesh Sahani:
Statistical inference for single- and multi-band Probabilistic Amplitude Demodulation. ICASSP 2010: 5466-5469
2000 – 2009
- 2009
- [j9]Pietro Berkes, Richard E. Turner, Maneesh Sahani:
A Structured Model of Video Reproduces Primary Visual Cortical Organisation. PLoS Comput. Biol. 5(9) (2009) - [c19]Jörg Lücke, Richard E. Turner, Maneesh Sahani, Marc Henniges:
Occlusive Components Analysis. NIPS 2009: 1069-1077 - 2008
- [j8]Jörg Lücke, Maneesh Sahani:
Maximal Causes for Non-linear Component Extraction. J. Mach. Learn. Res. 9: 1227-1267 (2008) - [c18]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: 5208-5211 - [c17]John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani:
Fast Gaussian process methods for point process intensity estimation. ICML 2008: 192-199 - [c16]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: 1881-1888 - 2007
- [j7]Richard E. Turner, Maneesh Sahani:
A Maximum-Likelihood Interpretation for Slow Feature Analysis. Neural Comput. 19(4): 1022-1038 (2007) - [c15]Richard E. Turner, Maneesh Sahani:
Probabilistic Amplitude Demodulation. ICA 2007: 544-551 - [c14]Jörg Lücke, Maneesh Sahani:
Generalized Softmax Networks for Non-linear Component Extraction. ICANN (1) 2007: 657-667 - [c13]Simon J. D. Prince, Jania Aghajanian, Umar Mohammed, Maneesh Sahani:
Latent Identity Variables: Biometric Matching Without Explicit Identity Estimation. ICB 2007: 424-434 - [c12]Byron M. Yu, John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani:
Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models. ICONIP (1) 2007: 586-595 - [c11]Misha B. Ahrens, Maneesh Sahani:
Inferring Elapsed Time from Stochastic Neural Processes. NIPS 2007: 1-8 - [c10]Pietro Berkes, Richard E. Turner, Maneesh Sahani:
On Sparsity and Overcompleteness in Image Models. NIPS 2007: 89-96 - [c9]John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani:
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes. NIPS 2007: 329-336 - [c8]Richard E. Turner, Maneesh Sahani:
Modeling Natural Sounds with Modulation Cascade Processes. NIPS 2007: 1545-1552 - 2005
- [j6]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): 1056-1067 (2005) - [j5]Kensuke Sekihara, Maneesh Sahani, Srikantan S. Nagarajan:
A simple nonparametric statistical thresholding for MEG spatial-filter source reconstruction images. NeuroImage 27(2): 368-376 (2005) - [c7]Byron M. Yu, Afsheen Afshar, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani:
Extracting Dynamical Structure Embedded in Neural Activity. NIPS 2005: 1545-1552 - 2003
- [j4]Maneesh Sahani, Peter Dayan:
Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity. Neural Comput. 15(10): 2255-2279 (2003) - [c6]Maneesh Sahani, Srikantan S. Nagarajan:
Reconstructing MEG Sources with Unknown Correlations. NIPS 2003: 693-700 - [c5]Maneesh Sahani:
A Biologically Plausible Algorithm for Reinforcement-shaped Representational Learning. NIPS 2003: 1287-1294 - 2002
- [c4]Maneesh Sahani, Jennifer F. Linden:
How Linear are Auditory Cortical Responses?. NIPS 2002: 109-116 - [c3]Peter Dayan, Maneesh Sahani, Gregoire Deback:
Adaptation and Unsupervised Learning. NIPS 2002: 221-228 - [c2]Maneesh Sahani, Jennifer F. Linden:
Evidence Optimization Techniques for Estimating Stimulus-Response Functions. NIPS 2002: 301-308 - 2000
- [j3]John S. Pezaris, Maneesh Sahani, Richard A. Andersen:
Spike train coherence in macaque parietal cortex during a memory saccade task. Neurocomputing 32-33: 953-960 (2000)
1990 – 1999
- 1999
- [j2]John S. Pezaris, Maneesh Sahani, Richard A. Andersen:
Response-locked changes in auto- and cross-covariations in parietal cortex. Neurocomputing 26-27: 471-476 (1999) - [j1]Michael Wehr, John S. Pezaris, Maneesh Sahani:
Simultaneous paired intracellular and tetrode recordings for evaluating the performance of spike sorting algorithms. Neurocomputing 26-27: 1061-1068 (1999) - 1997
- [c1]Maneesh Sahani, John S. Pezaris, Richard A. Andersen:
On the Separation of Signals from Neighboring Cells in Tetrode Recordings. NIPS 1997: 222-228
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from , , and to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-02 21:25 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint