The stock market is a very complex and volatile entity. Predicting it is a very challenging task but the emergence of machinelearning and the availability of large-scale stock market data provide an opportunity to le...
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machinelearning models are often built based on the notion that the labeled training data follows the same underlying distribution of the testing dataset. Often this assumption does not hold and the trained model'...
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ISBN:
(纸本)9798350371000;9798350370997
machinelearning models are often built based on the notion that the labeled training data follows the same underlying distribution of the testing dataset. Often this assumption does not hold and the trained model's performance becomes limited during the deployment in the target environment. To address this problem, we present a transfer learning-based targeted transfer learning (TTL) approach that employs the optimal transport distance (OTD) metric to facilitate knowledge transfer between the source and target domains. TTL method uses a target-side adaptation methodology by intentionally modifying the source domain data using a small number of target domain samples to improve transfer performance between source and target datasets. Experimental results on the benchmark datasets show the effectiveness of the proposed approach without re-training the source model on the target environment, which contributes to the selection of an appropriate source model and improves the performance of the target model.
The proposed work explores different machinelearning hyperparameter tuning techniques to maximize model performance. By systematically adjusting hyperparameters, such as learning rates, regularization strengths, and ...
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A recommendation predicts a consumer's preference for a product that has not yet been recommendations is to predict the interests of users and recommend product they want. Movie recommendations uses a variety of f...
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This research delves into the prompt identification and prediction of Polycystic Ovary Syndrome utilizing machinelearning, specifically focusing on the XGBoost algorithm. Through an examination of data gathered from ...
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Self-supervised learning has achieved state-of-the-art performance in various tasks and applications. In computer vision, self-supervised learning often employs contrastive learning and masked image modeling, each wit...
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ISBN:
(纸本)9798350364941;9798350364958
Self-supervised learning has achieved state-of-the-art performance in various tasks and applications. In computer vision, self-supervised learning often employs contrastive learning and masked image modeling, each with its limitations: contrastive learning heavily relies on strong data augmentation and large batch sizes, etc., while masked image modeling struggles to capture high-level semantics and discrimination. In this work, we introduce MAsked Contrastive Representation learning (MACRL), a novel framework that integrates both paradigms through an asymmetric siamese network design. The online and momentum branches of the network receive asymmetric data augmentation operations and extract features through their encoders. The decoder in the online branch reconstructs the original image, while the projectors in both branches compute the contrastive loss. The online branch and the momentum branch are updated through gradient backpropagation and exponential moving average, respectively. MACRL jointly optimizes the reconstruction and the contrastive objectives to encourage representations with enhanced discrimination and semantics. Experimental results show that MACRL achieves competitive performance in downstream vision tasks, including image classification and semantic segmentation. Moreover, it demonstrates consistent performance across both large-scale and small-scale datasets.
This study introduces a comprehensive framework to enhance distance estimation models in indoor environments by incorporating Bluetooth Low Energy (BLE) Anchor information along with Received Signal Strength Indicator...
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machinelearning (ML) has emerged as a constantly evolving and powerful technology with the potential to transform the field of information security. Its ability to handle enormous quantities of data at high speeds, d...
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machinelearning (ML) has become one of the most impactful fields of datascience in recent years. However, a significant concern with ML is its privacy risks due to rising attacks against ML models. Privacy-Preservin...
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ISBN:
(纸本)9798331522735;9798331522728
machinelearning (ML) has become one of the most impactful fields of datascience in recent years. However, a significant concern with ML is its privacy risks due to rising attacks against ML models. Privacy-Preserving machinelearning (PPML) methods have been proposed to mitigate the privacy and security risks of ML models. A popular approach to achieving PPML uses Homomorphic Encryption (HE). However, the highly publicized inefficiencies of HE make it unsuitable for highly scalable scenarios with resource-constrained devices. Hence, Hybrid Homomorphic Encryption (HHE) - a modern encryption scheme that combines symmetric cryptography with HE - has recently been introduced to overcome these challenges. HHE potentially provides a foundation to build new efficient and privacy-preserving services that transfer expensive HE operations to the cloud. This work introduces HHE to the ML field by proposing resource-friendly PPML protocols for edge devices. More precisely, we utilize HHE as the primary building block of our PPML protocols. We assess the performance of our protocols by first extensively evaluating each party's communication and computational cost on a dummy dataset and show the efficiency of our protocols by comparing them with similar protocols implemented using plain BFV. Subsequently, we demonstrate the real-world applicability of our construction by building an actual PPML application that uses HHE as its foundation to classify heart disease based on sensitive ECG data.
Artificial intelligence, together with machinelearning, Big data analysis, datascience, Mathematical modeling, Optimal control theory, and Simulation techniques, play very significant roles in the cutting-edge resea...
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