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» Image Distance Using Hidden Markov Models
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JMLR
2002
133views more  JMLR 2002»
13 years 7 months ago
Learning Precise Timing with LSTM Recurrent Networks
The temporal distance between events conveys information essential for numerous sequential tasks such as motor control and rhythm detection. While Hidden Markov Models tend to ign...
Felix A. Gers, Nicol N. Schraudolph, Jürgen S...
USS
2010
13 years 5 months ago
Acoustic Side-Channel Attacks on Printers
We examine the problem of acoustic emanations of printers. We present a novel attack that recovers what a dotmatrix printer processing English text is printing based on a record o...
Michael Backes, Markus Dürmuth, Sebastian Ger...
NIPS
2008
13 years 9 months ago
Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM
Neural activity is non-stationary and varies across time. Hidden Markov Models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. W...
Kentaro Katahira, Jun Nishikawa, Kazuo Okanoya, Ma...
ISBI
2008
IEEE
14 years 8 months ago
Estimation of cortical multivariate autoregressive models for EEG/MEG using an expectation-maximization algorithm
A new method for estimating multivariate autoregressive (MVAR) models of cortical connectivity from surface EEG or MEG measurements is presented. Conventional approaches to this p...
Bing Leung, Patrick Cheung, Barry D. Van Veen
IDA
2009
Springer
14 years 2 months ago
Estimating Markov Random Field Potentials for Natural Images
Markov Random Field (MRF) models with potentials learned from the data have recently received attention for learning the low-level structure of natural images. A MRF provides a pri...
Urs Köster, Jussi T. Lindgren, Aapo Hyvä...