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The ability to capture long-term dependencies in sequential data depends on the way context is represented.
Recognising human activities in sequential data from sensors is a challenging research area.
Firstly, I will propose a model, LSTM-Jump, that can skip unimportant information in sequential data, mimicking the skimming behavior of human reading.
Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture.
Not only Deep Learning methods are related to learning deep non-linear hierarchical features they can also be used to detect very long non-linear time dependencies in sequential data.
A recurrent neural network is an architecture of neural networks designed to make use of the symmetry across steps in sequential data while simultaneously at every step keeping track of the most salient information of previously seen steps, which may affect the interpretation of the current one [22].
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In this presentation I will focus on the most unique aspect of our work: adaptive scheduling of observations using the principles of Bayesian experimental design in a sequential data analysis setting.
Both systems provide quick direct access to individual collision events in a sequential data store of several terabytes, and they both considerably improve the event analysis efficiency.
We find that our metastate HMM approach enables a stronger HMM-based framework for the identification of complex structure in stochastic sequential data.
Conditional neural fields (CNF) [ 47] are probabilistic graphical models that have been extensively used in modeling sequential data [ 45, 49].
However, study participants were stratified by sex and prior exam results and subsequently randomised to two different data collection methods: Students in group A (sequential data collection as in cohort 1) provided posttest ratings regarding the 33 learning objectives printed on one list and, subsequently, thentest ratings regarding the same objectives on a second list.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com