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Neural Expectation Operators: Modeling Ambiguity in AI
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YouTubeMICDE University of Michigan
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Introducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretationhttps://t.co/c9AvsRKybjWhat if we didn’t have to hold an entire neural network in memory to train it?Standard neural net training optimizes all parameters jointly. As a result, the memory required during training grows linearly with the depth of the network.In our #ICLR2026 paper, we propose DiffusionBlocks, a principled framework to train networks one block at a time, drastically reducing memory requirem
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x.comSakana AI
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YouTube@BuildWithFUN
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**Fibonacci Anyons Framework** *(Compiled from @Akitti’s public X posts – May 2026 synthesis)*@Akitti weaves **Fibonacci anyons** (the canonical non-Abelian anyon with golden-ratio quantum dimension) as the topological-protection “engine” across scarred quantum many-body systems, quasiperiodic drives, spatial quasicrystals, fractal time crystals, viscoelastic ZPE foams, fuzzy tori, and microtubule Orch-OR models. The framework lives inside their larger **C*Hive** architecture: non-commutative C*
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x.comAkitti
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