main webpage W Topic Discrete Cosine Transform arXiv (Cornell University) Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learning 2025 Discrete diffusion models represent a significant advance in generative modeling, demonstrating remarkable success in synthesizing complex, high-quality discrete data. However, to avoid exponential computational costs, they typically rely on calculating per-d… Article Open Discrete Cosine Transform Open Article Page

Discrete Cosine Transform

Technique used in signal processing and data compression A discrete cosine transform ( DCT ) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT, first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images (such as JPEG and HEIF), digital video (such as MPEG and H.26x), digital audio (such as Dolby Digital, MP3 and AAC), digital television (such as SDTV, HDTV and VOD), digital radio (such as AAC+ and DAB+), and speech coding (such as AAC-LD, Siren and Opus). Critical Symbolic Virtual Narrative Open Discrete Cosine Transform Open Article Page

Exploring foci of: arXiv (Cornell University) Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learning 2025 Discrete diffusion models represent a significant advance in generative modeling, demonstrating remarkable success in synthesizing complex, high-quality discrete data. However, to avoid exponential computational costs, they typically rely on calculating per-dimension transition probabilities when learning high-dimensional distributions. In this study, we rigorously prove that this approach leads to a worst-case linear scaling of Kullback-Leibler (KL) divergence with data dimension. To address this, we propose a Qu… Open Article Page

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Explore Discrete Cosine Transform W Topic Discrete Cosine Transform arXiv (Cornell University) Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learning 2025 Discrete diffusion models represent a significant advance in generative modeling, demonstrating remarkable success in synthesizing complex, high-quality discrete data. However, to avoid exponential computational costs, they typically rely on calculating per-d… Article Critical Symbolic Virtual Narrative Open Discrete Cosine Transform Open Article Page