Variational Inference for Latent Space Models for Dynamic Networks

Electroencephalogram (EEG) is a neurotechnology used to measure brain activity via brain impulses. Throughout the years, EEG has contributed tremendously to data-driven research models (e.g., Generalised Linear Models, Bayesian Generative Models, and Latent Space Models) in Neuroscience Technology and Neuroinformatic. Due to versatility, portability, cost feasibility, and non-invasiveness. It contributed to various Neuroscientific data that led to advancement in medical, education, management, and even the marketing field. In the past years, the extensive uses of EEG have been inclined towards medical healthcare studies such as in disease detection and as an intervention in mental disorders, but not fully explored for uses in neuromarketing. Hence, this study construes the data acquisition technique in neuroscience studies using electroencephalogram and outlines the trend of revolution of this technique in aspects of its technology and databases by focusing on neuromarketing uses.

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Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding

Entropy ◽

10.3390/e22020213 ◽

2020 ◽

Vol 22 (2) ◽

pp. 213 ◽

Cited By ~ 1

Author(s):

Yiğit Uğur ◽

George Arvanitakis ◽

Abdellatif Zaidi

Keyword(s):

Neural Networks ◽

Lower Bound ◽

Gradient Descent ◽

Gaussian Mixture ◽

Variational Inference ◽

Stochastic Gradient Descent ◽

Information Bottleneck ◽

Latent Space ◽

Type Algorithm ◽

The Cost

In this paper, we develop an unsupervised generative clustering framework that combines the variational information bottleneck and the Gaussian mixture model. Specifically, in our approach, we use the variational information bottleneck method and model the latent space as a mixture of Gaussians. We derive a bound on the cost function of our model that generalizes the Evidence Lower Bound (ELBO) and provide a variational inference type algorithm that allows computing it. In the algorithm, the coders’ mappings are parametrized using neural networks, and the bound is approximated by Markov sampling and optimized with stochastic gradient descent. Numerical results on real datasets are provided to support the efficiency of our method.

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Modeling heterogeneity in random graphs through latent space models: a selective review

ESAIM Proceedings and Surveys ◽

10.1051/proc/201447004 ◽

2014 ◽

Vol 47 ◽

pp. 55-74 ◽

Cited By ~ 15

Author(s):

Catherine Matias ◽

Stéphane Robin

Keyword(s):

Random Graphs ◽

Selective Review ◽

Latent Space ◽

Latent Space Models

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Latent space models for multiview network data

The Annals of Applied Statistics ◽

10.1214/16-aoas955 ◽

2017 ◽

Vol 11 (3) ◽

pp. 1217-1244 ◽

Cited By ~ 3

Author(s):

Michael Salter-Townshend ◽

Tyler H. McCormick

Keyword(s):

Network Data ◽

Latent Space ◽

Latent Space Models

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Modeling reciprocity in social interactions with probabilistic latent space models

Natural Language Engineering ◽

10.1017/s1351324910000173 ◽

2011 ◽

Vol 17 (1) ◽

pp. 1-36 ◽

Cited By ~ 2

Author(s):

ROXANA GIRJU ◽

MICHAEL J. PAUL

Keyword(s):

Gender Differences ◽

Social Interactions ◽

Pattern Discovery ◽

Unsupervised Clustering ◽

Structural Level ◽

Latent Space ◽

Affective Value ◽

Latent Space Models ◽

The Web

AbstractReciprocity is a pervasive concept that plays an important role in governing people's behavior, judgments, and thus their social interactions. In this paper we present an analysis of the concept of reciprocity as expressed in English and a way to model it. At a larger structural level the reciprocity model will induce representations and clusters of relations between interpersonal verbs. In particular, we introduce an algorithm that semi-automatically discovers patterns encoding reciprocity based on a set of simple yet effective pronoun templates. Using the most frequently occurring patterns we queried the web and extracted 13,443 reciprocal instances, which represent a broad-coverage resource. Unsupervised clustering procedures are performed to generate meaningful semantic clusters of reciprocal instances. We also present several extensions (along with observations) to these models that incorporate meta-attributes like the verbs' affective value, identify gender differences between participants, consider the textual context of the instances, and automatically discover verbs with certain presuppositions. The pattern discovery procedure yields an accuracy of 97 per cent, while the clustering procedures – clustering with pairwise membership and clustering with transitions – indicate accuracies of 91 per cent and 64 per cent, respectively. Our affective value clustering can predict an unknown verb's affective value (positive, negative, or neutral) with 51 per cent accuracy, while it can discriminate between positive and negative values with 68 per cent accuracy. The presupposition discovery procedure yields an accuracy of 97 per cent.

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Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors (IDETC2021-71570)

Journal of Mechanical Design ◽

10.1115/1.4052221 ◽

2021 ◽

pp. 1-36

Author(s):

Liwei Wang ◽

Suraj Yerramilli ◽

Akshay Iyer ◽

Daniel Apley ◽

Ping Zhu ◽

...

Keyword(s):

Machine Learning ◽

Gaussian Processes ◽

Building Blocks ◽

Variational Inference ◽

Data Driven ◽

Ternary Oxide ◽

Latent Space ◽

Multiple Materials ◽

Qualitative Factors ◽

Gp Model

Abstract Scientific and engineering problems often require the use of artificial intelligence to aid understanding and the search for promising designs. While Gaussian processes (GP) stand out as easy-to-use and interpretable learners, they have difficulties in accommodating big datasets, qualitative inputs, and multi-type responses obtained from different simulators, which has become a common challenge for data-driven design applications. In this paper, we propose a GP model that utilizes latent variables and functions obtained through variational inference to address the aforementioned challenges simultaneously. The method is built upon the latent variable Gaussian process (LVGP) model where qualitative factors are mapped into a continuous latent space to enable GP modeling of mixed-variable datasets. By extending variational inference to LVGP models, the large training dataset is replaced by a small set of inducing points to address the scalability issue. Output response vectors are represented by a linear combination of independent latent functions, forming a flexible kernel structure to handle multi-type responses. Comparative studies demonstrate that the proposed method scales well for large datasets, while outperforming state-of-the-art machine learning methods without requiring much hyperparameter tuning. In addition, an interpretable latent space is obtained to draw insights into the effect of qualitative factors, such as those associated with “building blocks” of architectures and element choices in metamaterial and materials design. Our approach is demonstrated for machine learning of ternary oxide materials and topology optimization of a multiscale compliant mechanism with aperiodic microstructures and multiple materials.

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