5,938 research outputs found
Zero-error Slepian-Wolf Coding of Confined Correlated Sources with Deviation Symmetry
In this paper, we use linear codes to study zero-error Slepian-Wolf coding of
a set of sources with deviation symmetry, where the sources are generalization
of the Hamming sources over an arbitrary field. We extend our previous codes,
Generalized Hamming Codes for Multiple Sources, to Matrix Partition Codes and
use the latter to efficiently compress the target sources. We further show that
every perfect or linear-optimal code is a Matrix Partition Code. We also
present some conditions when Matrix Partition Codes are perfect and/or
linear-optimal. Detail discussions of Matrix Partition Codes on Hamming sources
are given at last as examples.Comment: submitted to IEEE Trans Information Theor
PATTERN: Pain Assessment for paTients who can't TEll using Restricted Boltzmann machiNe.
BackgroundAccurately assessing pain for those who cannot make self-report of pain, such as minimally responsive or severely brain-injured patients, is challenging. In this paper, we attempted to address this challenge by answering the following questions: (1) if the pain has dependency structures in electronic signals and if so, (2) how to apply this pattern in predicting the state of pain. To this end, we have been investigating and comparing the performance of several machine learning techniques.MethodsWe first adopted different strategies, in which the collected original n-dimensional numerical data were converted into binary data. Pain states are represented in binary format and bound with above binary features to construct (n + 1) -dimensional data. We then modeled the joint distribution over all variables in this data using the Restricted Boltzmann Machine (RBM).ResultsSeventy-eight pain data items were collected. Four individuals with the number of recorded labels larger than 1000 were used in the experiment. Number of avaliable data items for the four patients varied from 22 to 28. Discriminant RBM achieved better accuracy in all four experiments.ConclusionThe experimental results show that RBM models the distribution of our binary pain data well. We showed that discriminant RBM can be used in a classification task, and the initial result is advantageous over other classifiers such as support vector machine (SVM) using PCA representation and the LDA discriminant method
Purchasing-power depreciation under rising price level
Call number: LD2668 .R4 1963 C51
Probabilistic Federated Learning of Neural Networks Incorporated with Global Posterior Information
In federated learning, models trained on local clients are distilled into a
global model. Due to the permutation invariance arises in neural networks, it
is necessary to match the hidden neurons first when executing federated
learning with neural networks. Through the Bayesian nonparametric framework,
Probabilistic Federated Neural Matching (PFNM) matches and fuses local neural
networks so as to adapt to varying global model size and the heterogeneity of
the data. In this paper, we propose a new method which extends the PFNM with a
Kullback-Leibler (KL) divergence over neural components product, in order to
make inference exploiting posterior information in both local and global
levels. We also show theoretically that The additional part can be seamlessly
concatenated into the match-and-fuse progress. Through a series of simulations,
it indicates that our new method outperforms popular state-of-the-art federated
learning methods in both single communication round and additional
communication rounds situation
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