359.866 hasil untuk machine learning
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Differentiable programming in machine learning
…implementation methods. In the context of issues encountered in machine learning and deep learning, the forward mode is deemed more suitable as it efficiently differentiates functions with numerous inputs compared to outputs. Given Python's pivotal role in the ML landscape, the paper elaborates on…
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Machine Learning Potential for Serpentines
…samples and the large number of atoms in the unit cell. We developed a machine learning (ML) potential for serpentine minerals based on density functional theory calculation with the r2SCAN meta‐GGA functional for molecular dynamics simulation. We illustrate the success of this ML potential model…
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Earthquake prognosis using machine learning
…well as the environment. In this project, the successful application of machine learning techniques have been used for different elements of research which would be possible to use to make a more accurate short-term prognosis of upcoming earthquakes. Random Forest Classifier is the algorithm used…
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Machine learning in spectral domain
Theoretical aspects of automated learning from data involving deep neural networks have open questions. Here Giambagli et al. show that training the neural networks in the spectral domain of the network coupling matrices can reduce the amount of learning parameters and improve the pre-training…
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Harmonic Theory and Machine Learning
A natural inference mechanism is presented : the Black Box problem is transformed into a Dirichlet's problem on the closed cube. Then it is solved in closed polynomial form, together with a Mean-Value theorem and a Maximum Principle.A generalization to Polytopes and a reduction of any Dirichlet…
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Machine learning Post-Minkowskian integrals
…physical observables in gauge and gravity theories. We show that such a machine learning approach improves the convergence of the Monte Carlo algorithm for high-precision evaluation of multi-dimensional integrals compared to traditional algorithms. In particular, we use a neural network to improve…
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Machine learning in solar physics
Abstract The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from…
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Modeling hadronization using machine learning
We present the first steps in the development of a new class of hadronization models utilizing machine learning techniques. We successfully implement, validate, and train a conditional sliced-Wasserstein autoencoder to replicate the Pythia generated kinematic distributions of first-hadron…
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Group-Invariant Quantum Machine Learning
Quantum machine learning (QML) models are aimed at learning from data encoded in quantum states. Recently, it has been shown that models with little to no inductive biases (i.e., with no assumptions about the problem embedded in the model) are likely to have trainability and generalization issues…
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Machine Learning Approaches for Metalloproteins
…there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of…
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Machine Learning Techniques for Calorimetry
…the energies and positions of electrons and photons. Even though several Machine Learning (ML) algorithms have been already used for calorimetry, with the constant advancement of the field, more and more sophisticated techniques have become available, which can be beneficial for object…
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Quantum Machine Learning with SQUID
In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with…
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Synergic quantum generative machine learning
Abstract We introduce a new approach towards generative quantum machine learning significantly reducing the number of hyperparameters and report on a proof-of-principle experiment demonstrating our approach. Our proposal depends on collaboration between the generators and discriminator, thus, we…
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Machine Learning for Additive Manufacturing
…pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many…
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Machine Learning for Energy Systems
The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and…
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Shadows of quantum machine learning
Abstract Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of quantum machine learning models in practice is that these models, even…
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Forecasting with Machine Learning Techniques
The decision-maker is increasingly utilising machine learning (ML) techniques to find patterns in huge quantities of real-time data [...]
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