Related papers: Deep Learning and AdS/QCD
Deep learning applies hierarchical layers of hidden variables to construct nonlinear high dimensional predictors. Our goal is to develop and train deep learning architectures for spatio-temporal modeling. Training a deep architecture is…
Recent advances in deep learning have achieved impressive gains in classification accuracy on a variety of types of data, including images and text. Despite these gains, however, concerns have been raised about the calibration, robustness,…
We discuss applications of gauge/gravity duality to describe the spectrum of light hadrons. We compare two particular 5-dimensional approaches: a model with an infrared deformed Anti-de Sitter metric and another one based on a dynamical…
Deep neural networks have revolutionized machine learning, yet their training dynamics remain theoretically unclear-we develop a continuous-time, matrix-valued stochastic differential equation (SDE) framework that rigorously connects the…
Estimates of the light hadron masses, decay constants and couplings in AdS/QCD models are generally more accurate than should have been expected. Certain predictions based on the AdS/CFT correspondence, such as the ratio of the equilibrium…
Holographic QCD is an extra-dimensional approach to modeling hadrons, the bound states of the strong interactions. In holographic models, the extra spatial dimension creates a waveguide for fields, and the discrete towers of modes…
Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can…
New AdS/QCD backgrounds have been proposed to describe the spectrum of heavy vector mesons via the implementation of additional energy scales on the bulk geometry of the soft wall model. The extra energy scales are needed to include the…
We present a framework for generative machine learning that leverages the holographic principle of quantum gravity, or to be more precise its manifestation as the anti-de Sitter/conformal field theory (AdS/CFT) correspondence, with…
According to AdS/DL (Anti de Sitter/ Deep Learning) correspondence given by \cite{Has}, in this paper with a data-driven approach and leveraging holography principle we have designed an artificial neural network architecture to produce…
Deep learning (DL)-based autoencoder is a potential architecture to implement end-to-end communication systems. In this letter, we first give a brief introduction to the autoencoder-represented communication system. Then, we propose a novel…
The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches. A deep continual learning algorithm, namely…
Deep Learning has become one of the primary research areas in developing intelligent machines. Most of the well-known applications (such as Speech Recognition, Image Processing and NLP) of AI are driven by Deep Learning. Deep Learning…
We are interested to explore the limit in using deep learning (DL) to study the electromagnetic response for complex and random metasurfaces, without any specific applications in mind. For simplicity, we focus on a simple pure reflection…
Accurate diagnosis of Alzheimer's disease (AD) is both challenging and time consuming. With a systematic approach for early detection and diagnosis of AD, steps can be taken towards the treatment and prevention of the disease. This study…
We derive an estimator of the spectral density of a functional time series that is the output of a multilayer perceptron neural network. The estimator is motivated by difficulties with the computation of existing spectral density estimators…
Modeling of turbulent flows is still challenging. One way to deal with the large scale separation due to turbulence is to simulate only the large scales and model the unresolved contributions as done in large-eddy simulation (LES). This…
The paper considers the problem of deep-learning-based classification of digitally modulated signals using I/Q data and studies the generalization ability of a trained neural network (NN) to correctly classify digitally modulated signals it…
Deep representation learning is a crucial procedure in multimedia analysis and attracts increasing attention. Most of the popular techniques rely on convolutional neural network and require a large amount of labeled data in the training…
We discuss an AdS / QCD model that consider anomalous dimensions. The effect of this kind of dimensions is considered as a mass term that depend on holographical coordenate for duals modes in bulk.