Results 21 to 30 of about 11,467,392 (329)
Machine learning active-nematic hydrodynamics [PDF]
Proceedings of the National Academy of Sciences, 2021 Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of ...
Jonathan Colen +12 more
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MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research
npj Computational Materials, 2022 Research and games both require the participant to make a series of choices. Active learning is a process borrowed from machine learning for algorithmically making choices that has become increasingly used to accelerate materials research.
Keith A. Brown
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Homomorphic Encryption-Based Federated Privacy Preservation for Deep Active Learning
Entropy, 2022 Active learning is a technique for maximizing performance of machine learning with minimal labeling effort and letting the machine automatically and adaptively select the most informative data for labeling.
Hendra Kurniawan, Masahiro Mambo
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A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
Applied Sciences, 2023 In recent years, the ubiquity of social networks has transformed them into essential platforms for information dissemination. However, the unmoderated nature of social networks and the advent of advanced machine learning techniques, including generative ...
Feng Yi +3 more
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Nature Communications, 2023 Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures.
Tao Wang +14 more
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Active Learning for Neural Machine Translation
SSRN Electronic Journal, 2023 The machine translation mechanism translates texts automatically between different natural languages, and Neural Machine Translation (NMT) has gained attention for its rational context analysis and fluent translation accuracy. However, processing low-resource languages that lack relevant training attributes like supervised data is a current challenge ...
Neeraj Vashistha +2 more
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A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions
IEEE Access, 2022 Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and various sources.
Supriya V. Mahadevkar +6 more
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Enabling robust offline active learning for machine learning potentials using simple physics-based priors [PDF]
Machine Learning: Science and Technology, 2020 Machine learning surrogate models for quantum mechanical simulations have enabled the field to efficiently and accurately study material and molecular systems.
M. Shuaibi +3 more
semanticscholar +1 more source
Greedy structure learning from data that contain systematic missing values [PDF]
, 2022 Learning from data that contain missing values represents a common phenomenon in many domains. Relatively few Bayesian Network structure learning algorithms account for missing data, and those that do tend to rely on standard approaches that assume ...
Liu, Y +5 more
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Transductive confidence machine for active learning [PDF]
Proceedings of the International Joint Conference on Neural Networks, 2003., 2004 This paper describes a novel active learning strategy using universal p-value measures of confidence based on algorithmic randomness, and transconductive inference. The early stopping criterion for active learning is based on the bias-variance tradeoff for classification.
Shen-Shyang Ho, Harry Wechsler
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