52,434 research outputs found
Excitonic Wave Function Reconstruction from Near-Field Spectra Using Machine Learning Techniques
A general problem in quantum mechanics is the reconstruction of eigenstate
wave functions from measured data. In the case of molecular aggregates,
information about excitonic eigenstates is vitally important to understand
their optical and transport properties. Here we show that from spatially
resolved near field spectra it is possible to reconstruct the underlying
delocalized aggregate eigenfunctions. Although this high-dimensional nonlinear
problem defies standard numerical or analytical approaches, we have found that
it can be solved using a convolutional neural network. For both one-dimensional
and two-dimensional aggregates we find that the reconstruction is robust to
various types of disorder and noise
Understanding Hidden Memories of Recurrent Neural Networks
Recurrent neural networks (RNNs) have been successfully applied to various
natural language processing (NLP) tasks and achieved better results than
conventional methods. However, the lack of understanding of the mechanisms
behind their effectiveness limits further improvements on their architectures.
In this paper, we present a visual analytics method for understanding and
comparing RNN models for NLP tasks. We propose a technique to explain the
function of individual hidden state units based on their expected response to
input texts. We then co-cluster hidden state units and words based on the
expected response and visualize co-clustering results as memory chips and word
clouds to provide more structured knowledge on RNNs' hidden states. We also
propose a glyph-based sequence visualization based on aggregate information to
analyze the behavior of an RNN's hidden state at the sentence-level. The
usability and effectiveness of our method are demonstrated through case studies
and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2017
Application of support vector machines on the basis of the first Hungarian bankruptcy model
In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks
- …