Timezone: »
A number of recent scientific and engineering problems require signals to be decomposed into a product of a slowly varying positive envelope and a quickly varying carrier whose instantaneous frequency also varies slowly over time. Although signal processing provides algorithms for so-called amplitude- and frequency-demodulation (AFD), there are well known problems with all of the existing methods. Motivated by the fact that AFD is ill-posed, we approach the problem using probabilistic inference. The new approach, called probabilistic amplitude and frequency demodulation (PAFD), models instantaneous frequency using an auto-regressive generalization of the von Mises distribution, and the envelopes using Gaussian auto-regressive dynamics with a positivity constraint. A novel form of expectation propagation is used for inference. We demonstrate that although PAFD is computationally demanding, it outperforms previous approaches on synthetic and real signals in clean, noisy and missing data settings.
Author Information
Richard Turner (University of Cambridge)
Maneesh Sahani (Gatsby Unit, UCL)
Related Events (a corresponding poster, oral, or spotlight)
-
2011 Poster: Probabilistic amplitude and frequency demodulation »
Mon. Dec 12th 06:00 -- 10:59 PM Room
More from the Same Authors
-
2021 Spotlight: Probabilistic Tensor Decomposition of Neural Population Spiking Activity »
Hugo Soulat · Sepiedeh Keshavarzi · Troy Margrie · Maneesh Sahani -
2022 : Ice Core Dating using Probabilistic Programming »
Aditya Ravuri · Tom Andersson · Ieva Kazlauskaite · William Tebbutt · Richard Turner · Scott Hosking · Neil Lawrence · Markus Kaiser -
2022 : Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement »
Tom Andersson · Wessel Bruinsma · Efstratios Markou · Daniel C. Jones · Scott Hosking · James Requeima · Anna Vaughan · Anna-Louise Ellis · Matthew Lazzara · Richard Turner -
2022 : Contextual Squeeze-and-Excitation »
Massimiliano Patacchiola · John Bronskill · Aliaksandra Shysheya · Katja Hofmann · Sebastian Nowozin · Richard Turner -
2022 : FiT: Parameter Efficient Few-shot Transfer Learning »
Aliaksandra Shysheya · John Bronskill · Massimiliano Patacchiola · Sebastian Nowozin · Richard Turner -
2022 : Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners »
Elre Oldewage · John Bronskill · Richard Turner -
2022 : Panel »
Erin Grant · Richard Turner · Neil Houlsby · Priyanka Agrawal · Abhijeet Awasthi · Salomey Osei -
2022 Poster: Structured Recognition for Generative Models with Explaining Away »
Changmin Yu · Hugo Soulat · Neil Burgess · Maneesh Sahani -
2022 Poster: Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification »
Massimiliano Patacchiola · John Bronskill · Aliaksandra Shysheya · Katja Hofmann · Sebastian Nowozin · Richard Turner -
2021 Poster: How Tight Can PAC-Bayes be in the Small Data Regime? »
Andrew Foong · Wessel Bruinsma · David Burt · Richard Turner -
2021 Poster: Collapsed Variational Bounds for Bayesian Neural Networks »
Marcin Tomczak · Siddharth Swaroop · Andrew Foong · Richard Turner -
2021 Poster: Memory Efficient Meta-Learning with Large Images »
John Bronskill · Daniela Massiceti · Massimiliano Patacchiola · Katja Hofmann · Sebastian Nowozin · Richard Turner -
2021 Poster: Probabilistic Tensor Decomposition of Neural Population Spiking Activity »
Hugo Soulat · Sepiedeh Keshavarzi · Troy Margrie · Maneesh Sahani -
2020 Poster: Efficient Low Rank Gaussian Variational Inference for Neural Networks »
Marcin Tomczak · Siddharth Swaroop · Richard Turner -
2020 Poster: Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes »
Andrew Foong · Wessel Bruinsma · Jonathan Gordon · Yann Dubois · James Requeima · Richard Turner -
2020 Poster: On the Expressiveness of Approximate Inference in Bayesian Neural Networks »
Andrew Foong · David Burt · Yingzhen Li · Richard Turner -
2020 Poster: VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data »
Chao Ma · Sebastian Tschiatschek · Richard Turner · José Miguel Hernández-Lobato · Cheng Zhang -
2020 Poster: Non-reversible Gaussian processes for identifying latent dynamical structure in neural data »
Virginia Rutten · Alberto Bernacchia · Maneesh Sahani · Guillaume Hennequin -
2020 Oral: Non-reversible Gaussian processes for identifying latent dynamical structure in neural data »
Virginia Rutten · Alberto Bernacchia · Maneesh Sahani · Guillaume Hennequin -
2020 Poster: Organizing recurrent network dynamics by task-computation to enable continual learning »
Lea Duncker · Laura N Driscoll · Krishna V Shenoy · Maneesh Sahani · David Sussillo -
2020 Poster: Continual Deep Learning by Functional Regularisation of Memorable Past »
Pingbo Pan · Siddharth Swaroop · Alexander Immer · Runa Eschenhagen · Richard Turner · Mohammad Emtiyaz Khan -
2020 Oral: Continual Deep Learning by Functional Regularisation of Memorable Past »
Pingbo Pan · Siddharth Swaroop · Alexander Immer · Runa Eschenhagen · Richard Turner · Mohammad Emtiyaz Khan -
2019 Poster: A neurally plausible model for online recognition and postdiction in a dynamical environment »
Li Kevin Wenliang · Maneesh Sahani -
2019 Poster: Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model »
Wenbo Gong · Sebastian Tschiatschek · Sebastian Nowozin · Richard Turner · José Miguel Hernández-Lobato · Cheng Zhang -
2019 Poster: A neurally plausible model learns successor representations in partially observable environments »
Eszter Vértes · Maneesh Sahani -
2019 Oral: A neurally plausible model learns successor representations in partially observable environments »
Eszter Vértes · Maneesh Sahani -
2019 Poster: Practical Deep Learning with Bayesian Principles »
Kazuki Osawa · Siddharth Swaroop · Mohammad Emtiyaz Khan · Anirudh Jain · Runa Eschenhagen · Richard Turner · Rio Yokota -
2019 Poster: Kernel Instrumental Variable Regression »
Rahul Singh · Maneesh Sahani · Arthur Gretton -
2019 Oral: Kernel Instrumental Variable Regression »
Rahul Singh · Maneesh Sahani · Arthur Gretton -
2018 Poster: Infinite-Horizon Gaussian Processes »
Arno Solin · James Hensman · Richard Turner -
2018 Poster: Flexible and accurate inference and learning for deep generative models »
Eszter Vértes · Maneesh Sahani -
2018 Poster: Geometrically Coupled Monte Carlo Sampling »
Mark Rowland · Krzysztof Choromanski · François Chalus · Aldo Pacchiano · Tamas Sarlos · Richard Turner · Adrian Weller -
2018 Poster: Temporal alignment and latent Gaussian process factor inference in population spike trains »
Lea Duncker · Maneesh Sahani -
2018 Spotlight: Geometrically Coupled Monte Carlo Sampling »
Mark Rowland · Krzysztof Choromanski · François Chalus · Aldo Pacchiano · Tamas Sarlos · Richard Turner · Adrian Weller -
2017 Poster: Streaming Sparse Gaussian Process Approximations »
Thang Bui · Cuong Nguyen · Richard Turner -
2017 Poster: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning »
Shixiang (Shane) Gu · Timothy Lillicrap · Richard Turner · Zoubin Ghahramani · Bernhard Schölkopf · Sergey Levine -
2016 Poster: Rényi Divergence Variational Inference »
Yingzhen Li · Richard Turner -
2015 Poster: Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) »
Mijung Park · Wittawat Jitkrittum · Ahmad Qamar · Zoltan Szabo · Lars Buesing · Maneesh Sahani -
2015 Poster: Neural Adaptive Sequential Monte Carlo »
Shixiang (Shane) Gu · Zoubin Ghahramani · Richard Turner -
2015 Poster: Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels »
Felipe Tobar · Thang Bui · Richard Turner -
2015 Poster: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2015 Spotlight: Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels »
Felipe Tobar · Thang Bui · Richard Turner -
2015 Spotlight: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2014 Poster: Tree-structured Gaussian Process Approximations »
Thang Bui · Richard Turner -
2014 Spotlight: Tree-structured Gaussian Process Approximations »
Thang Bui · Richard Turner -
2013 Workshop: Acquiring and Analyzing the Activity of Large Neural Ensembles »
Srinivas C Turaga · Lars Buesing · Maneesh Sahani · Jakob H Macke -
2013 Poster: Extracting regions of interest from biological images with convolutional sparse block coding »
Marius Pachitariu · Adam M Packer · Noah Pettit · Henry Dalgleish · Michael Hausser · Maneesh Sahani -
2013 Poster: Recurrent linear models of simultaneously-recorded neural populations »
Marius Pachitariu · Biljana Petreska · Maneesh Sahani -
2013 Spotlight: Recurrent linear models of simultaneously-recorded neural populations »
Marius Pachitariu · Biljana Petreska · Maneesh Sahani -
2012 Poster: Spectral learning of linear dynamics from generalised-linear observations with application to neural population data »
Lars Buesing · Jakob H Macke · Maneesh Sahani -
2012 Oral: Spectral learning of linear dynamics from generalised-linear observations with application to neural population data »
Lars Buesing · Jakob H Macke · Maneesh Sahani -
2012 Poster: Learning visual motion in recurrent neural networks »
Marius Pachitariu · Maneesh Sahani -
2011 Oral: Empirical models of spiking in neural populations »
Jakob H Macke · Lars Buesing · John P Cunningham · Byron M Yu · Krishna V Shenoy · Maneesh Sahani -
2011 Poster: Empirical models of spiking in neural populations »
Jakob H Macke · Lars Buesing · John P Cunningham · Byron M Yu · Krishna V Shenoy · Maneesh Sahani -
2011 Poster: Dynamical segmentation of single trials from population neural data »
Biljana Petreska · Byron M Yu · John P Cunningham · Gopal Santhanam · Stephen I Ryu · Krishna V Shenoy · Maneesh Sahani -
2010 Session: The Sam Roweis Symposium »
Maneesh Sahani -
2009 Poster: Occlusive Components Analysis »
Jörg Lücke · Richard Turner · Maneesh Sahani · Marc Henniges -
2008 Poster: Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity »
Byron M Yu · John P Cunningham · Gopal Santhanam · Stephen I Ryu · Krishna V Shenoy · Maneesh Sahani -
2007 Workshop: Beyond Simple Cells: Probabilistic Models for Visual Cortical Processing »
Richard Turner · Pietro Berkes · Maneesh Sahani -
2007 Oral: Inferring Elapsed Time from Stochastic Neural Processes »
Misha B Ahrens · Maneesh Sahani -
2007 Spotlight: Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes »
John P Cunningham · Byron M Yu · Krishna V Shenoy · Maneesh Sahani -
2007 Poster: Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes »
John P Cunningham · Byron M Yu · Krishna V Shenoy · Maneesh Sahani -
2007 Poster: Inferring Elapsed Time from Stochastic Neural Processes »
Misha B Ahrens · Maneesh Sahani -
2007 Poster: Modeling Natural Sounds with Modulation Cascade Processes »
Richard Turner · Maneesh Sahani -
2007 Poster: On Sparsity and Overcompleteness in Image Models »
Pietro Berkes · Richard Turner · Maneesh Sahani