Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

27.0K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
27.0K
What is Variation?01:14

What is Variation?

17.6K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
17.6K
Control Volume and System Representations01:16

Control Volume and System Representations

1.5K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.5K
State Space Representation01:27

State Space Representation

536
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
536
Variation01:19

Variation

7.7K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
7.7K
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

921
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
921

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Combining clinical notes with structured electronic health records enhances the prediction of mental health crises.

Cell reports. Medicine·2023
Same author

In-Network Learning: Distributed Training and Inference in Networks.

Entropy (Basel, Switzerland)·2023
Same author

On the Information Bottleneck Problems: Models, Connections, Applications and Information Theoretic Views.

Entropy (Basel, Switzerland)·2020
Same author

Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding.

Entropy (Basel, Switzerland)·2020
Same author

Information Theory for Data Communications and Processing.

Entropy (Basel, Switzerland)·2020
Same author

Rate-Distortion Region of a Gray-Wyner Model with Side Information.

Entropy (Basel, Switzerland)·2020

Related Experiment Video

Updated: Jan 21, 2026

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

14.4K

Distributed Variational Representation Learning.

Inaki Estella Aguerri, Abdellatif Zaidi

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 23, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study generalizes the Information Bottleneck (IB) method for distributed representation learning. It introduces new algorithms to optimize compressed representations from multiple sources for enhanced information retrieval.

    More Related Videos

    New Variations for Strategy Set-shifting in the Rat
    09:45

    New Variations for Strategy Set-shifting in the Rat

    Published on: January 23, 2017

    8.6K
    Drosophila Adult Olfactory Shock Learning
    09:48

    Drosophila Adult Olfactory Shock Learning

    Published on: August 7, 2014

    29.1K

    Related Experiment Videos

    Last Updated: Jan 21, 2026

    Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
    08:31

    Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

    Published on: July 31, 2016

    14.4K
    New Variations for Strategy Set-shifting in the Rat
    09:45

    New Variations for Strategy Set-shifting in the Rat

    Published on: January 23, 2017

    8.6K
    Drosophila Adult Olfactory Shock Learning
    09:48

    Drosophila Adult Olfactory Shock Learning

    Published on: August 7, 2014

    29.1K

    Area of Science:

    • Information Theory
    • Machine Learning
    • Data Science

    Background:

    • Distributed representation learning involves processing multiple information sources (X1,...,XK) to infer ground truth (Y).
    • The centralized Information Bottleneck (IB) method is a key framework for single-source information compression.
    • Extending IB to distributed settings presents challenges in optimizing collective representations.

    Purpose of the Study:

    • To generalize the Information Bottleneck (IB) method to the distributed setting for learning from multiple information sources.
    • To establish theoretical characterizations of the complexity-relevance tradeoff in distributed representation learning.
    • To develop and evaluate algorithms for computing optimal distributed representations.

    Main Methods:

    • Information-theoretic analysis of distributed representation learning.
    • Generalization of the centralized Information Bottleneck (IB) method to K ≥ 2 encoders.
    • Study of discrete memoryless (DM) and memoryless vector Gaussian data models.
    • Development of a variational bound generalizing the evidence lower bound (ELBO) to the distributed setting.
    • Implementation of Blahut-Arimoto type and variational inference type algorithms.

    Main Results:

    • Established a single-letter characterization for the optimal complexity-relevance tradeoff in discrete memoryless sources.
    • Provided an explicit characterization of the optimal complexity-relevance tradeoff for vector Gaussian models.
    • Developed a novel variational bound for distributed representation learning.
    • Demonstrated the efficiency of proposed algorithms on synthetic and real datasets through numerical results.

    Conclusions:

    • The proposed generalization of IB effectively addresses distributed representation learning.
    • The developed algorithms provide practical methods for computing optimal distributed representations.
    • The study offers significant theoretical and algorithmic contributions to information theory and machine learning.