178,711 research outputs found
voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data
Relationships in scientific data, such as the numerical and spatial
distribution relations of features in univariate data, the scalar-value
combinations' relations in multivariate data, and the association of volumes in
time-varying and ensemble data, are intricate and complex. This paper presents
voxel2vec, a novel unsupervised representation learning model, which is used to
learn distributed representations of scalar values/scalar-value combinations in
a low-dimensional vector space. Its basic assumption is that if two scalar
values/scalar-value combinations have similar contexts, they usually have high
similarity in terms of features. By representing scalar values/scalar-value
combinations as symbols, voxel2vec learns the similarity between them in the
context of spatial distribution and then allows us to explore the overall
association between volumes by transfer prediction. We demonstrate the
usefulness and effectiveness of voxel2vec by comparing it with the isosurface
similarity map of univariate data and applying the learned distributed
representations to feature classification for multivariate data and to
association analysis for time-varying and ensemble data.Comment: Accepted by IEEE Transaction on Visualization and Computer Graphics
(TVCG
Too much information is no information: how machine learning and feature selection could help in understanding the motor control of pointing
© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The aim of this study was to develop the use of Machine Learning techniques as a means of multivariate analysis in studies of motor control. These studies generate a huge amount of data, the analysis of which continues to be largely univariate. We propose the use of machine learning classification and feature selection as a means of uncovering feature combinations that are altered between conditions. High dimensional electromyogram (EMG) vectors were generated as several arm and trunk muscles were recorded while subjects pointed at various angles above and below the gravity neutral horizontal plane. We used Linear Discriminant Analysis (LDA) to carry out binary classifications between the EMG vectors for pointing at a particular angle, vs. pointing at the gravity neutral direction. Classification success provided a composite index of muscular adjustments for various task constraints—in this case, pointing angles. In order to find the combination of features that were significantly altered between task conditions, we conducted a post classification feature selection i.e., investigated which combination of features had allowed for the classification. Feature selection was done by comparing the representations of each category created by LDA for the classification. In other words computing the difference between the representations of each class. We propose that this approach will help with comparing high dimensional EMG patterns in two ways; (i) quantifying the effects of the entire pattern rather than using single arbitrarily defined variables and (ii) identifying the parts of the patterns that convey the most information regarding the investigated effects.Peer reviewe
A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
Multinomial probit models are widely-implemented representations which allow
both classification and inference by learning changes in vectors of class
probabilities with a set of p observed predictors. Although various frequentist
methods have been developed for estimation, inference and classification within
such a class of models, Bayesian inference is still lagging behind. This is due
to the apparent absence of a tractable class of conjugate priors, that may
facilitate posterior inference on the multinomial probit coefficients. Such an
issue has motivated increasing efforts toward the development of effective
Markov chain Monte Carlo methods, but state-of-the-art solutions still face
severe computational bottlenecks, especially in large p settings. In this
article, we prove that the entire class of unified skew-normal (SUN)
distributions is conjugate to a wide variety of multinomial probit models, and
we exploit the SUN properties to improve upon state-of-art-solutions for
posterior inference and classification both in terms of closed-form results for
key functionals of interest, and also by developing novel computational methods
relying either on independent and identically distributed samples from the
exact posterior or on scalable and accurate variational approximations based on
blocked partially-factorized representations. As illustrated in a
gastrointestinal lesions application, the magnitude of the improvements
relative to current methods is particularly evident, in practice, when the
focus is on large p applications
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
Active Learning for discriminative models has largely been studied with the
focus on individual samples, with less emphasis on how classes are distributed
or which classes are hard to deal with. In this work, we show that this is
harmful. We propose a method based on the Bayes' rule, that can naturally
incorporate class imbalance into the Active Learning framework. We derive that
three terms should be considered together when estimating the probability of a
classifier making a mistake for a given sample; i) probability of mislabelling
a class, ii) likelihood of the data given a predicted class, and iii) the prior
probability on the abundance of a predicted class. Implementing these terms
requires a generative model and an intractable likelihood estimation.
Therefore, we train a Variational Auto Encoder (VAE) for this purpose. To
further tie the VAE with the classifier and facilitate VAE training, we use the
classifiers' deep feature representations as input to the VAE. By considering
all three probabilities, among them especially the data imbalance, we can
substantially improve the potential of existing methods under limited data
budget. We show that our method can be applied to classification tasks on
multiple different datasets -- including one that is a real-world dataset with
heavy data imbalance -- significantly outperforming the state of the art
Learning Bilingual Word Representations by Marginalizing Alignments
We present a probabilistic model that simultaneously learns alignments and
distributed representations for bilingual data. By marginalizing over word
alignments the model captures a larger semantic context than prior work relying
on hard alignments. The advantage of this approach is demonstrated in a
cross-lingual classification task, where we outperform the prior published
state of the art.Comment: Proceedings of ACL 2014 (Short Papers
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
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