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learn generative ai
gans, diffusion, and large language models — the theory and the code to ship.
curatedintermediate~5 weeks, part-time
generative ai foundations
how machines learn to create — gans, diffusion, and llms — paired with the canonical code repos so you can generate, not just read.
4 modules · 12 resources · checkpoint per modulestay current
see the full digest →what's new in generative ai
- Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decodingthis paper presents set diffusion, a method that combines autoregressive and diffusion models for faster and more flexible decoding of discrete tokens. ml engineers working with sequence generation can use this to improve the efficiency and control over their generative models.
- A Physics-guided Fine-tuned LLM-based Framework for Customized Power Distribution System Feeder Generationthis research proposes a physics-guided llm framework to generate customized power distribution system feeders. this can help engineers design more efficient and reliable power grids by automating the generation of complex system configurations.
- Unified Multimodal Autoregressive Modeling with Shared Context—Visual Tokenizer is Key to Unificationthis paper presents a unified multimodal autoregressive model that uses a shared context and visual tokenizer for unification. ai developers can use this approach to build more cohesive and capable multimodal models that can process and generate across different data types like text and images.
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