Maneesh Sahani
34 papers · 2007–2025 · 5 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Interdisciplinary Bridge π Renaissance Researcher (9) πΊοΈ Taxonomy Completionist (25) π£ Hot Topic Early Bird
π
Academic Marathon
(18)
π§
Keyword Pioneer
π
Conference Polyglot
(5)
π
Keyword Trendsetter Combo
(6)
π
Conference Loyalist
(26)
π
Keyword Champion
(2)
π¬
Deep Specialist
(11)
π±
Topic Pioneer
π
Century Club
(34)
π₯
Unstoppable
(8)
ποΈ
Keyword Collector
(94)
π
Trend Setter
Conferences
NIPS (26)
ICLR (3)
ICML (2)
JMLR (2)
AISTATS (1)
Top co-authors
Research topics
Keywords
gaussian process
(7)
latent variable model
(6)
variational inference
(6)
neural population
(6)
generative model
(6)
bayesian inference
(5)
gaussian process factor analysis
(4)
dynamical system
(4)
unsupervised learning
(3)
reinforcement learning
(3)
sparse coding
(3)
successor representation
(3)
spiking activity
(2)
factor analysis
(2)
continual learning
(2)
point process
(2)
wake-sleep algorithm
(2)
expectation maximization
(2)
statistical model
(2)
dimensionality reduction
(2)
Papers
Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA
ICLR 2025
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset
NIPS 2024
Successor-Predecessor Intrinsic Exploration
NIPS 2023
Minimum Description Length Control
ICLR 2023
Unsupervised representation learning with recognition-parametrised probabilistic models
AISTATS 2023
A State Representation for Diminishing Rewards
NIPS 2023
Structured Recognition for Generative Models with Explaining Away
NIPS 2022
A First-Occupancy Representation for Reinforcement Learning
ICLR 2022
Probabilistic Tensor Decomposition of Neural Population Spiking Activity
NIPS 2021
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
NIPS 2020
Amortised Learning by Wake-Sleep
ICML 2020
Organizing recurrent network dynamics by task-computation to enable continual learning
NIPS 2020
Kernel Instrumental Variable Regression
NIPS 2019
A neurally plausible model for online recognition and postdiction in a dynamical environment
NIPS 2019
Learning interpretable continuous-time models of latent stochastic dynamical systems
ICML 2019
A neurally plausible model learns successor representations in partially observable environments
NIPS 2019
Temporal alignment and latent Gaussian process factor inference in population spike trains
NIPS 2018
Flexible and accurate inference and learning for deep generative models
NIPS 2018
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
NIPS 2015
Efficient Occlusive Components Analysis
JMLR 2014
Recurrent linear models of simultaneously-recorded neural populations
NIPS 2013
Extracting regions of interest from biological images with convolutional sparse block coding
NIPS 2013
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data
NIPS 2012
Learning visual motion in recurrent neural networks
NIPS 2012
Probabilistic amplitude and frequency demodulation
NIPS 2011
Empirical models of spiking in neural populations
NIPS 2011
Dynamical segmentation of single trials from population neural data
NIPS 2011
Occlusive Components Analysis
NIPS 2009
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity
NIPS 2008
Maximal Causes for Non-linear Component Extraction
JMLR 2008
On Sparsity and Overcompleteness in Image Models
NIPS 2007
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes
NIPS 2007
Modeling Natural Sounds with Modulation Cascade Processes
NIPS 2007
Inferring Elapsed Time from Stochastic Neural Processes
NIPS 2007