← Authors

Maneesh Sahani

34 papers · 2007–2025 · 5 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (9) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (9) πŸ—ΊοΈ Taxonomy Completionist (25) πŸƒ Academic Marathon (18) 🧭 Keyword Pioneer 🌍 Conference Polyglot (5) 🌟 Keyword Trendsetter Combo (6) 🏠 Conference Loyalist (26) πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (11) 🌱 Topic Pioneer πŸ’Ž Century Club (34) πŸ”₯ Unstoppable (8) πŸ—ƒοΈ Keyword Collector (94) πŸ“ˆ Trend Setter

Conferences

NIPS (26) ICLR (3) ICML (2) JMLR (2) AISTATS (1)

Top co-authors

Krishna V. Shenoy (5) Changmin Yu (4) John P. Cunningham (4) Richard Turner (4) Byron M. Yu (4) Ted Moskovitz (3) Hugo Soulat (3) Marius Pachitariu (3) Lars Buesing (3) Lea Duncker (3)

Research topics

Processing (1)

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