Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is ineff...
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In this paper, we introduce a new class of score-based generative models (SGMs) designed to handle high-cardinality data distributions by leveraging concepts from mean-field theory. We present mean-field chaos diffusi...
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In this paper, we introduce a new class of score-based generative models (SGMs) designed to handle high-cardinality data distributions by leveraging concepts from mean-field theory. We present mean-field chaos diffusion models (MF-CDMs), which address the curse of dimensionality inherent in high-cardinality data by utilizing the propagation of chaos property of interacting particles. By treating high-cardinality data as a large stochastic system of interacting particles, we develop a novel score-matching method for infinite-dimensional chaotic particle systems and propose an approximation scheme that employs a subdivision strategy for efficient training. Our theoretical and empirical results demonstrate the scalability and effectiveness of MF-CDMs for managing large high-cardinality data structures, such as 3D point clouds. Copyright 2024 by the author(s)
Using dispersed data and training, federated learning (FL) moves AI capabilities to edge devices or does tasks locally. Many consider FL the start of a new era in AI, yet it is still immature. FL has not garnered the ...
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作者：
Saad, Mohammed AyadJaafar, RosminaChellappan, Kalaivani
Faculty of Engineering and Built Environment Department of Electrical Electronics and System Engineering Selangor Bangi43600 Malaysia Al-Kitab University
Department of Medical Instrumentations Technique Engineering Kirkuk36001 Iraq Universitas Airlangga
Biomedical Engineering Study Program Faculty of Science and Technology Surabaya60115 Indonesia
Efficient data collection in wireless sensor networks (WSNs) is crucial. While traditional approaches rely on stationary data sinks, the use of mobile sinks, like unmanned aerial vehicles (UAVs), has shown promise in ...
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India witnessed 34.42 % growth of electric two-wheelers (E2W) in the third quarter of fiscal year 2024. The average solar incidence across the country ranges between 4 - 7 kWh/sq.m/day. Adoption of electric vehicles c...
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The recent changes in consumption patterns and the development of the Internet have increased the diversity of user feedback in the recommender system. As a result, recent studies have highlighted the complementary in...
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In federated learning, clients cooperatively train a global model by training local models over their datasets under the coordination of a central server. However, clients may sometimes be unavailable for training due...
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Quantum receivers aim to effectively navigate the vast quantum-state space to endow quantum information processing capabilities unmatched by classical *** date,only a handful of quantum receivers have been constructed...
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Quantum receivers aim to effectively navigate the vast quantum-state space to endow quantum information processing capabilities unmatched by classical *** date,only a handful of quantum receivers have been constructed to tackle the problem of discriminating coherent *** receivers designed by analytical approaches,however,are incapable of effectively adapting to diverse environmental conditions,resulting in their quickly diminishing performance as the operational complexities ***,we present a general architecture,dubbed the quantum receiver enhanced by adaptive learning,to adapt quantum receiver structures to diverse operational *** adaptively learned quantum receiver is experimentally implemented in a hardware platform with record-high *** the architecture and the experimental advances,the error rate is reduced up to 40%over the standard quantum limit in two coherent-state encoding schemes.
Autonomous drone racing competitions serve as a testing ground for enhancing the perceptual, planning, and control aspects of micro unmanned aerial vehicles (MAVs). This study thoroughly outlines the strategy, methodo...
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In neural speech enhancement,a mismatch exists between the training objective,i.e.,Mean-Square Error(MSE),and perceptual quality evaluation metrics,i.e.,perceptual evaluation of speech quality and short-time objective...
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In neural speech enhancement,a mismatch exists between the training objective,i.e.,Mean-Square Error(MSE),and perceptual quality evaluation metrics,i.e.,perceptual evaluation of speech quality and short-time objective *** propose a novel reinforcement learning algorithm and network architecture,which incorporate a non-differentiable perceptual quality evaluation metric into the objective function using a dynamic filter *** the traditional dynamic filter implementation that directly generates a convolution kernel,we use a filter generation agent to predict the probability density function of a multivariate Gaussian distribution,from which we sample the convolution *** results show that the proposed reinforcement learning method clearly improves the perceptual quality over other supervised learning methods with the MSE objective function.
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