3.327 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Deep Learning in Protein Structural Modeling and Design
function" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.
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A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
Machine learning algorithms are increasingly used in various remote sensing applications due to their ability to identify nonlinear correlations. Ensemble algorithms have been included in many practical applications to improve prediction accuracy. We provide an overview of three widely used…
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Deep Learning for PET Image Reconstruction
This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for the reconstruction of images in positron emission tomography (PET). Deep learning can be used either directly or as a component of conventional reconstruction, in order to reconstruct images from…
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Learning Molecular Representations for Medicinal Chemistry
…relationships for drug discovery. Now, advances in deep learning make it possible to efficiently and compactly learn molecular representations directly from data. In this review, we discuss how active research in molecular deep learning can address limitations of current descriptors and…
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Adversarial Reciprocal Points Learning for Open Set Recognition
Open set recognition (OSR), aiming to simultaneously classify the seen classes and identify the unseen classes as 'unknown', is essential for reliable machine learning. The key challenge of OSR is how to reduce the empirical classification risk on the labeled known data and the open space risk on…
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Artikel Jurnal Teks lengkap
Review: Deep Learning on 3D Point Clouds
…as robotics, autonomous driving, and augmented and virtual reality. Deep learning is now the most powerful tool for data processing in computer vision and is becoming the most preferred technique for tasks such as classification, segmentation, and detection. While deep learning techniques are…
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Democratising deep learning for microscopy with ZeroCostDL4Mic
Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility…
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Solar Power Forecasting Using Deep Learning Techniques
…power, in the short term, of photovoltaic power plants, by means of deep learning techniques. To fulfill the above, a deep learning technique based on the Long Short Term Memory (LSTM) algorithm is evaluated with respect to its ability to forecast solar power data. An evaluation of the performance…
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Artikel Jurnal Teks lengkap
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in Fan et al. [Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package gpumd. We increase the accuracy of NEP models both by…
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Deep Reinforcement Learning for Autonomous Driving: A Survey
With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a…
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Biological network analysis with deep learning
…tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying…
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Artikel Jurnal Teks lengkap
A General Framework for Uncertainty Estimation in Deep Learning
…to detect such failures automatically is fundamental to integrate deep learning algorithms into robotics. Current approaches for uncertainty estimation of neural networks require changes to the network and optimization process, typically ignore prior knowledge about the data, and tend to make…
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Artikel Jurnal Teks lengkap
Deep learning methods in protein structure prediction
Protein Structure Prediction is a central topic in Structural Bioinformatics. Since the '60s statistical methods, followed by increasingly complex Machine Learning and recently Deep Learning methods, have been employed to predict protein structural information at various levels of detail. In this…
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Understanding deep learning (still) requires rethinking generalization
Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small gap between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family or to the regularization techniques used during…
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Confident Learning: Estimating Uncertainty in Dataset Labels
Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of…
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Artikel Jurnal Teks lengkap
A critical examination of compound stability predictions from machine-learned formation energies
Abstract Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include…
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