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Diffusion model

Technique for the generative modeling of a continuous probability distribution

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Artificial Intelligence (3)
Machine Learning (2)
Computer Vision (1)
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Diffusion models (2) · arXiv (2) · Bird-SR (1) · Super-resolution (1) · Reward Feedback Learning (1) · Image processing (1) · Machine learning (1) · Meta-modeling (1) · LLM activations (1) · Residual stream (1) · Neural network analysis (1) · Mechanistic interpretability (1) · DECO (1) · Diffusion Transformer (1) · Bimanual Manipulation (1) · Tactile Sensing (1) · LoRA Adapter (1) · Multimodal Learning (1) · Dexterous Robotics (1)

📖 Key Information

In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of two major components: the forward diffusion process, and the reverse sampling process. The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset.

📰 Related News (3)

🇺🇸 Bidirectional Reward-Guided Diffusion for Real-World Image Super-Resolution (2026-02-10)
arXiv:2602.07069v1 Announce Type: cross Abstract: Diffusion-based super-resolution can synthesize rich details, but models trained on synthetic paire...
🇺🇸 Learning a Generative Meta-Model of LLM Activations (2026-02-09)
arXiv:2602.06964v1 Announce Type: cross Abstract: Existing approaches for analyzing neural network activations, such as PCA and sparse autoencoders, ...
🇺🇸 DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter (2026-02-07)
arXiv:2602.05513v1 Announce Type: cross Abstract: Overview of the Proposed DECO Framework.} DECO is a DiT-based policy that decouples multimodal cond...

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🌐 Deco (disambiguation) (1 shared articles)
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