Understanding Autoencoders: Applications and Properties
Autoencoders play a crucial role in supervised and unsupervised learning, with applications ranging from image classification to denoising and watermark removal. They compress input data into a latent space and reconstruct it to produce valuable embeddings. Autoencoders are data-specific, lossy, and
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Sparse Millimeter-Wave Imaging Using Compressed Sensing and Point Spread Function Calibration
A novel indoor millimeter-wave imaging system based on sparsity estimated compressed sensing and calibrated point spread function is introduced. The system utilizes a unique calibration procedure to process the point spread function acquired from measuring a suspended point scatterer. By estimating
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Understanding Matrix Factorization for Latent Factor Recovery
Explore the concept of matrix factorization for recovering latent factors in a matrix, specifically focusing on user ratings of movies. This technique involves decomposing a matrix into multiple matrices to extract hidden patterns and relationships. The process is crucial for tasks like image denois
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Advanced Convolution Denoising Techniques for Large-Volume Seebeck Calorimeters
Cutting-edge research on convolution denoising methods for Seebeck calorimeters to reduce noise levels caused by temperature fluctuations. The study explores hardware design, mathematical principles, and examples of denoising applications, aiming to enhance measurement accuracy and stability in larg
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Denoising-Oriented Deep Hierarchical Reinforcement Learning for Next-basket Recommendation
This research paper presents a novel approach, HRL4Ba, for Next-basket Recommendation (NBR) by addressing the challenge of guiding recommendations based on historical baskets that may contain noise products. The proposed Hierarchical Reinforcement Learning framework incorporates dynamic context mode
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