69,197 research outputs found
Unsupervised Generative Modeling Using Matrix Product States
Generative modeling, which learns joint probability distribution from data
and generates samples according to it, is an important task in machine learning
and artificial intelligence. Inspired by probabilistic interpretation of
quantum physics, we propose a generative model using matrix product states,
which is a tensor network originally proposed for describing (particularly
one-dimensional) entangled quantum states. Our model enjoys efficient learning
analogous to the density matrix renormalization group method, which allows
dynamically adjusting dimensions of the tensors and offers an efficient direct
sampling approach for generative tasks. We apply our method to generative
modeling of several standard datasets including the Bars and Stripes, random
binary patterns and the MNIST handwritten digits to illustrate the abilities,
features and drawbacks of our model over popular generative models such as
Hopfield model, Boltzmann machines and generative adversarial networks. Our
work sheds light on many interesting directions of future exploration on the
development of quantum-inspired algorithms for unsupervised machine learning,
which are promisingly possible to be realized on quantum devices.Comment: 11 pages, 12 figures (not including the TNs) GitHub Page:
https://congzlwag.github.io/UnsupGenModbyMPS
Towards correct-by-construction product variants of a software product line: GFML, a formal language for feature modules
Software Product Line Engineering (SPLE) is a software engineering paradigm
that focuses on reuse and variability. Although feature-oriented programming
(FOP) can implement software product line efficiently, we still need a method
to generate and prove correctness of all product variants more efficiently and
automatically. In this context, we propose to manipulate feature modules which
contain three kinds of artifacts: specification, code and correctness proof. We
depict a methodology and a platform that help the user to automatically produce
correct-by-construction product variants from the related feature modules. As a
first step of this project, we begin by proposing a language, GFML, allowing
the developer to write such feature modules. This language is designed so that
the artifacts can be easily reused and composed. GFML files contain the
different artifacts mentioned above.The idea is to compile them into FoCaLiZe,
a language for specification, implementation and formal proof with some
object-oriented flavor. In this paper, we define and illustrate this language.
We also introduce a way to compose the feature modules on some examples.Comment: In Proceedings FMSPLE 2015, arXiv:1504.0301
Computational Batik Motif Generation: Innovation of Traditional Heritage by Fractal Computation\ud
Human-computer interaction has been the cause of the emerging innovations in many fields, including in design and art, architectural, technological artifacts, and even traditional heritage. In the case of Indonesian traditional heritages, the computation of fractal designs has been introduced to develop batik design – the genuine textile art and skill that becomes a symbol of Indonesian culture. The uniqueness of Batik, which depicted in the richness of its motifs, is regarded as one of interesting aspect to be researched and innovated using computational techniques. Recent studies of batik motifs have discovered conjecture to the existence of fractal geometry in batik designs. This finding has given some inspiration of implementing certain fractal concepts, such escape-time fractal (complex plane) and iterated function system to generate batik motifs. We develop motif generator based upon the Collage Theorem by using Java TM platform. This software is equipped by interface that can be used by user to generate basic patterns, which could be interpreted and painted as batik motif. Experimentally, we found that computationally generated fractal motifs are appropriated to be implemented as batik motif. However, human made batik motifs are less detail and some of them differ significantly with the computationally generated ones for tools used to draw batik and human aesthetic constraints
Tensor Monte Carlo: particle methods for the GPU era
Multi-sample, importance-weighted variational autoencoders (IWAE) give
tighter bounds and more accurate uncertainty estimates than variational
autoencoders (VAE) trained with a standard single-sample objective. However,
IWAEs scale poorly: as the latent dimensionality grows, they require
exponentially many samples to retain the benefits of importance weighting.
While sequential Monte-Carlo (SMC) can address this problem, it is
prohibitively slow because the resampling step imposes sequential structure
which cannot be parallelised, and moreover, resampling is non-differentiable
which is problematic when learning approximate posteriors. To address these
issues, we developed tensor Monte-Carlo (TMC) which gives exponentially many
importance samples by separately drawing samples for each of the latent
variables, then averaging over all possible combinations. While the sum
over exponentially many terms might seem to be intractable, in many cases it
can be computed efficiently as a series of tensor inner-products. We show that
TMC is superior to IWAE on a generative model with multiple stochastic layers
trained on the MNIST handwritten digit database, and we show that TMC can be
combined with standard variance reduction techniques
Pathophysiology, histopathology and therapeutic of SARS-CoV-2
The rapid transmission of SARS-CoV-2 through the world has induced the scientist to understand the histopathology of the virus and then to find an effective drug. However, many of the point associated with the virus pathogenicity still unknown and need more studies. In this chapter the pathophysiology, histopathology and therapeutic of SARS-CoV-2 has been reviewed. It was appeared that pathogenicity of SARS-CoV-2 is belonging to the viral with genome structure which acting by blocking the host innate immune response. Both chloroquine and hydroxyl-chloroquine have similar structure and mechanism action and they are among the most effective antiviral for treating the patents with the SARS-CoV-2. Chloroquine works by inhibition the intracellular organism by increasing the pH
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