What is "neural entropy" in physics-based diffusion models? скачать в хорошем качестве

What is "neural entropy" in physics-based diffusion models?

🎲 FREE Machine Learning Course: https://compu-flair.com/physics-inspi... 🚀 Apply to our bootcamp: https://compu-flair.com/bootcamp In this video, Dr. Ardavan (Ahmad) Borzou explains neural entropy as an "information bookkeeping" tool to account for the cost of generating structure from noise. Neural entropy is the measure of guidance a network must supply to reverse the loss of distinguishability inherent in the noising process. By utilizing KL divergence and learned steering signals, the diffusion models bias random fluctuations toward patterns found in training data. The video further explores how different noise schedules change this "information bill" by altering the rate at which structural data is erased. Ultimately, this perspective frames generative AI not as simple randomness, but as the active injection of guidance capacity required to compensate for the irreversibility of the diffusion process. 📚 A useful reference: • Neural Entropy: https://arxiv.org/abs/2409.03817 📺 Chapters 00:00 - Hidden Cost of AI Images 00:26 - Free ML Courses 01:07 - Information Loss in Diffusion 01:40 - Defining Neural Entropy 02:49 - How Noising Erases Information 04:01 - Entropy and Irreversibility 05:34 - Using KL Divergence for Model's Guidance 06:18 - How Diffusion models work 07:24 - Neural Entropy and Noise Scheduling 08:51 - Image Reconstruction Costs 09:56 - The Image Destruction and Construction Cycle 10:42 - Physics Perspective of Neural Entropy 11:10 - Diffusion Models and Informational Work