Publications related to Variational autoencoder

Bridging the Past, Present and Future: Modeling Scene Activities From Event Relationships and Global Rules

Jean-Marc Odobez, Rémi Emonet, Jagannadan Varadarajan

This paper addresses the discovery of activities and learns the underlying processes that govern their occurrences over time in complex surveillance scenes. To this end, we propose a novel topic model that accounts for the two main factors that affect thes ...

2012

Variational Learning for Recurrent Spiking Networks

Wulfram Gerstner, Danilo Jimenez Rezende, Daniël Pieter Wierstra

We derive a plausible learning rule updating the synaptic efficacies for feedforward, feedback and lateral connections between observed and latent neurons. Operating in the context of a generative model for distributions of spike sequences, the learning me ...

2011

A New Hybrid Bayesian-Variational Particle Filter With Application To Mitotic Cell Tracking

Michaël Unser, Ricard Delgado Gonzalo

Tracking algorithms are traditionally based on either a variational approach or a Bayesian one. In the variational case, a cost function is established between two consecutive frames and minimized by standard optimization algorithms. In the Bayesian case, ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

A New Hybrid Bayesian-Variational Particle Filter with Application to Mitotic Cell Tracking

Michaël Unser, Ricard Delgado Gonzalo

Tracking algorithms are traditionally based on either a variational approach or a Bayesian one. In the variational case, a cost function is established between two consecutive frames and minimized by standard optimization algorithms. In the Bayesian case, ...

IEEE2011

Evolving Complete Robots with CPPN-NEAT: The Utility of Recurrent Connections

Joshua Evan Auerbach

This paper extends prior work using Compositional Pattern Producing Networks (CPPNs) as a generative encoding for the purpose of simultaneously evolving robot morphology and control. A method is presented for translating CPPNs into complete robots includin ...

ACM Press2011

Visual Speaker Localization Aided by Acoustic Models

The following paper presents a novel audio-visual approach for unsupervised speaker locationing. Using recordings from a single, low-resolution room overview camera and a single far-field microphone, a state-of-the art audio-only speaker localization syste ...

2009

Noise-Resilient Group Testing: Limitations and Constructions

Mahdi Cheraghchi Bashi Astaneh

We study combinatorial group testing schemes for learning

d

-sparse boolean vectors using highly unreliable disjunctive measurements. We consider an adversarial noise model that only limits the number of false observations, and show that any noise-resilie ...

Springer2009

Learning sparse generative models of audiovisual signals

Pierre Vandergheynst, Gianluca Monaci

This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that ...

2008

On Learning the Statistical Representation of a Task and Generalizing it to Various Contexts

Aude Billard, Sylvain Calinon

This paper presents an architecture for solving generically the problem of extracting the relevant features of a given task in a programming by demonstration framework and the problem of generalizing the acquired knowledge to various contexts. We validate ...

2006

Spectral Entropy Feature in Multi-stream for Robust ASR

Hervé Bourlard, Hemant Misra

In recent papers, entropy computed from sub-bands of the spectrum was used as a feature for automatic speech recognition. In the present paper, we further study the sub-band spectral entropy features which can give the flatness/peakiness of the sub-band sp ...

IDIAP2005

Variational autoencoder

Graph Chatbot

Chat with Graph Search

Bridging the Past, Present and Future: Modeling Scene Activities From Event Relationships and Global Rules

Variational Learning for Recurrent Spiking Networks

A New Hybrid Bayesian-Variational Particle Filter With Application To Mitotic Cell Tracking

A New Hybrid Bayesian-Variational Particle Filter with Application to Mitotic Cell Tracking

Evolving Complete Robots with CPPN-NEAT: The Utility of Recurrent Connections

Visual Speaker Localization Aided by Acoustic Models

Noise-Resilient Group Testing: Limitations and Constructions

Learning sparse generative models of audiovisual signals

On Learning the Statistical Representation of a Task and Generalizing it to Various Contexts

Spectral Entropy Feature in Multi-stream for Robust ASR

A New Hybrid Bayesian-Variational Particle Filter with Application to Mitotic Cell Tracking

Visual Speaker Localization Aided by Acoustic Models

Variational Learning for Recurrent Spiking Networks

Bridging the Past, Present and Future: Modeling Scene Activities From Event Relationships and Global Rules

Evolving Complete Robots with CPPN-NEAT: The Utility of Recurrent Connections

Noise-Resilient Group Testing: Limitations and Constructions

A New Hybrid Bayesian-Variational Particle Filter With Application To Mitotic Cell Tracking

Learning sparse generative models of audiovisual signals

On Learning the Statistical Representation of a Task and Generalizing it to Various Contexts

Spectral Entropy Feature in Multi-stream for Robust ASR