204,674 results for machine learning
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Quantum Machine Learning—Quo Vadis?
The book Quantum Machine Learning: What Quantum Computing Means to Data Mining, by Peter Wittek, made quantum machine learning popular to a wider audience. The promise of quantum machine learning for big data is that it will lead to new applications due to the exponential speed-up and the…
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Integrating fNIRS and machine learning
…to other neuroimaging modalities. Besides the integration of fNIRS and machine learning, a contribution of this work is that various approaches were implemented and tested to find the implementation that achieves the highest performance. All the implementations used a logistic regression model for…
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Machine Learning on Mainstream Microcontrollers
This paper presents the Edge Learning Machine (ELM), a machine learning framework for edge devices, which manages the training phase on a desktop computer and performs inferences on microcontrollers. The framework implements, in a platform-independent C language, three supervised machine learning…
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Machine Learning in Renewable Energy
The volume of energy produced and consumed from renewable sources increases by a significant rate both in absolute value and also as a proportion of the total energy produced and consumed [...]
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Machine Learning for Multimedia Communications
Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the…
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Machine Learning for Biomedical Application
The tremendous development of technology also affects medical science, including imaging diagnostics [...]
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Soil Science-Informed Machine Learning
Machine learning (ML) applications in soil science have significantly increased over the past two decades, reflecting a growing trend towards data-driven research addressing soil security. This extensive application has mainly focused on enhancing predictions of soil properties, particularly soil…
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Simulating Hadronization with Machine Learning
…there remain phenomenological areas where they do not match data well. A machine-learning-based alternative called MLhad, intended ultimately to be data-trainable, can simulate hadronization by encoding latentspace vectors, trained to be distributed according to a user-defined distribution using…
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Anticipating synchronization with machine learning
…data driven. We show that this challenging problem can be addressed with machine learning. In particular, exploiting reservoir computing or echo state networks, we employ a “parameter-aware” scheme to train the neural machine using time series acquired from a small number of distinct asynchronous…
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Casimir effect with machine learning
…We propose a different numerical approach to this problem based on machine-learning techniques and illustrate the effectiveness of the method in a (2+1)-dimensional scalar field theory. The Casimir energy is first calculated numerically using a Monte Carlo algorithm for a set of the Dirichlet…
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Machine Learning Applications in Dentistry
…recent studies in this area and envisions future directions on the use of machine learning in dental problems.
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Machine Learning for Data Linkage
Data linkage traditionally uses deterministic and probabilistic methods. Alternatively, machine learning methods can be applied as classification algorithms, using the data to inform decisions. This project compared the quality, in terms of precision and recall, of traditional methods with selected…
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Machine Learning for Dissimulating Reality
In the last decade, advances in statistical modeling and computer science have boosted the production of machine-produced contents in different fields: from language to image generation, the quality of the generated outputs is remarkably high, sometimes better than those produced by a human being…
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Machine learning empowered drug discovery
Traditional drug discovery strategies include lead molecule identification, lead optimization, preclinical studies and clinical trials. The pharmaceutical and biotechnology research and development (R&D) department spends more than 10 years and $1 billion to bring the molecule to market…
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Machine learning enhanced cell tracking
…and assemble these into meaningful trajectories. In this aspect, machine learning (ML) is having a transformational effect on bioimage analysis, now enabling robust cell detection in multidimensional image data. However, the task of cell tracking, or constructing accurate multi-generational…
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Pulse-efficient quantum machine learning
Quantum machine learning algorithms based on parameterized quantum circuits are promising candidates for near-term quantum advantage. Although these algorithms are compatible with the current generation of quantum processors, device noise limits their performance, for example by inducing an…
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Discovering invariants via machine learning
Invariants and conservation laws convey critical information about the underlying dynamics of a system, yet it is generally infeasible to find them from large-scale data without any prior knowledge or human insight. We propose ConservNet to achieve this goal, a neural network that spontaneously…
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Machine learning universal bosonic functionals
…This simplification of the search approach allows us to use standard machine learning methods to perform a quite efficient computation of both F[γ] and its functional derivative. For the Bose-Hubbard model, we present a comparison between our approach and the quantum Monte Carlo method.
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Machine Learning Applications in Seismology
The comprehension of earthquakes and natural hazards, including volcanic eruptions and landslides, as well as explosions, through observational data is a pivotal activity within the field of seismology [...]
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Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors
…comparisons exist between different tools. We compared a non-machine learning (non-ML) tool (ImageScope, Leica Biosystems) with a machine learning (ML) tool (Aiforia Create, Aiforia Technologies) on Ki67-stained slides from 10 low proliferative neuroendocrine tumor cases (Ki67 score
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