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Modified SINNs achieve superior accuracy in high-dimensional PDEs, outperforming traditional spectral methods and standard PINNs.
Forget retraining: OrthoFuse lets you merge separately trained style and subject adapters for diffusion models *without any further training*, unlocking efficient compositionality.
Sparse autoencoders, hyped as a key interpretability tool, may not be learning much more than random feature sets, casting doubt on their ability to decompose model internals.
