Categorical data classification and clustering are essential to many fields, including pattern recognition, data mining, knowledge discovery, and machinelearning. It is crucial to understand how to provide categorica...
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In recent years, statistical analysis has revolution-ized decision-making in the NBA and the growth of Fantasy Basketball platforms like DraftKings and FanDuel. This paper presents a novel approach to draft a fantasy ...
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Federated learning epitomizes a sophisticated distributed machinelearning methodology, enabling collaborative neural network model training across multiple entities without necessitating the transfer of local data, t...
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ISBN:
(纸本)9798400709234
Federated learning epitomizes a sophisticated distributed machinelearning methodology, enabling collaborative neural network model training across multiple entities without necessitating the transfer of local data, thereby fortifying data privacy protection. A significant challenge in federated learning lies in the statistical heterogeneity, characterized by non-independent and identically distributed (Non-IID) local data across diverse parties. This heterogeneity can engender inconsistent optimization within individual local models. Although previous research has endeavored to tackle issues stemming from heterogeneous data, our findings indicate that these attempts have not yielded high-performance neural network models. To confront this fundamental challenge, we introduce the FedRL framework in this paper, which facilitates efficient federated learning through review learning. The core principle of FedRL involves leveraging the knowledge representation generated by the global and local model layers to conduct periodic layer-by-layer comparative learning in a reciprocal manner. This strategy rectifies local model training, leading to enhanced outcomes. Our experimental results and subsequent analysis substantiate that FedRL effectively augments model accuracy in image classification tasks, while demonstrating resilience to statistical heterogeneity across all participating entities.
The increasing capabilities of machinelearning (ML) models go hand in hand with an immense amount of data and computational power required for training. Therefore, training is usually outsourced into HPC facilities, ...
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ISBN:
(纸本)9798331541378
The increasing capabilities of machinelearning (ML) models go hand in hand with an immense amount of data and computational power required for training. Therefore, training is usually outsourced into HPC facilities, where we have started to experience limits in scaling conventional hardware, as theorized by Moore's law. Despite heavy parallelization and optimization efforts, current state-of-the-art ML models require weeks for training, which is associated with an enormous CO2 footprint. Quantum computing, and specifically Quantum machinelearning (QML), can offer significant theoretical speed-ups and enhanced expressive power. However, training QML models requires tuning various hyperparameters, which is a nontrivial task, and suboptimal choices can highly affect the trainability and performance of the models. In this study, we identify main hyperparameters and collect data about the performance of QML models on real-world data. Notably, we build on existing work by also benchmarking different proposed parameter initialization strategies, which should help avoid trainability issues, such as Barren Plateaus. We compare different configurations and provide researchers with performance data and concrete suggestions for hyperparameter selection.
machinelearning is a branch of Artificial Intelligence (AI) and computer technology which address on the usage or application of data and Algorithms to emulate the style that humans learn, gradually improving its acc...
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The recent developments in mobile computing have facilitated the application of on-device machinelearning on edge devices. However, complex heterogeneous environmental factors such as network conditions, spatial loca...
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Depth information is critical for highly developed robotic systems which need the global perception of their surroundings. A light field camera records with a large amount of data, increasing potentials for the depth ...
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ISBN:
(纸本)9798400709234
Depth information is critical for highly developed robotic systems which need the global perception of their surroundings. A light field camera records with a large amount of data, increasing potentials for the depth estimation improvements. This paper addresses the challenge of performing depth estimation from a light field image: preserving depth discontinuity, which provides robotics with the more precise perception. We design an end-to-end trainable Pyramid Depth Estimation Network PDE-Net, by introducing the multiscale feature merging from stacked images along four directions of light fields to perform the coarse-to-fine estimation. Extensive experiments on both synthetic light field datasets and real-world light field datasets demonstrate the superior performance of the proposed learning-based method compared to the state-of-the-art methods.
Lung cancer is the leading cause of death worldwide for both men and women. The most well-known disease is the heart disease followed by lung cancer. Early discovery of lung cancer can effectively treat lung cancer at...
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In modern agriculture, accurately anticipating crop yield estimation is critical aids of sustainable resource management, efficient decision-making, and food security. This is therefore a process that includes a break...
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Classical machinelearning models struggle with learning and prediction tasks on data sets exhibiting long-range correlations. To quantify this observation we introduce a new quantity we call strong k-contextuality, d...
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ISBN:
(纸本)9798331541378
Classical machinelearning models struggle with learning and prediction tasks on data sets exhibiting long-range correlations. To quantify this observation we introduce a new quantity we call strong k-contextuality, develop efficient algorithms to estimate the strong k-contextuality of an empirical data set, and prove that the presence of strong k-contextuality lower bounds the classical resources required to model the associated distribution. We also show that this correlation measure does not induce a similar resource lower bound for quantum generative models, and thus propose strong k-contextuality as an empirical measure for evaluating whether a given machinelearning task may be better suited for quantum models than classical models.
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