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Provably private tabular classification is now achievable with a single forward pass, outperforming baselines and drastically cutting fitting time.
Single-neuron models can now capture multimodal voltage responses, including coexisting firing/non-firing states and depolarization block modes, a feat previously requiring massive ensembles or failing with neural operators.
Earth observation embeddings can boost weather downscaling accuracy by over 11% by effectively encoding persistent surface properties.
A learned diffusion model can achieve a perfect match to the target distribution while exhibiting an arbitrarily large $L^2$ score error, challenging existing training paradigms.
Unlock probabilistic PDE modeling from existing deterministic backbones with a simple retrofitting technique that slashes error by up to 54% and works across diverse architectures.
Diffusion models degrade predictably when trained on their own outputs, but a little fresh data can keep them on track.
