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ORCIDManeesh Sahani
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2020 – today
- 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
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