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tech MIT 6.7960 導讀 (Fall 2024 OCW)

MIT 6.7960 L14: Generative Models Basics — Density/Energy Models, GANs, Autoregressive, Diffusion

Generative models learn the data distribution p(x). Density models model probability directly, energy models use an unnormalized potential + sampler, GANs let a discriminator force realistic samples, autoregressive predicts the next token step by step, and diffusion dodges tricky maximum-likelihood via 'add noise then learn to denoise'.

#mit-67960#deep-learning#generative-model#gan#diffusion#autoregressive#fall-2024
ai deep-dive Reading Stanford CS230

Adversarial Robustness and Generative Models: It's Not Nonlinearity, It's Linearity

Researchers initially assumed neural networks are easy to fool because they're nonlinear. That was wrong — Goodfellow's 2014 paper argues the primary cause is their linear nature, and high dimensionality lets every tiny perturbation compound. The second half covers generative models: GANs' three pathologies, and why diffusion sidesteps two of them by adding noise and learning to remove it.

#adversarial-attack#prompt-injection#gan#diffusion-models#stanford-cs230