Denoising Neural Processes model noisy inputs during meta-learning
On real air-quality sensors, DnNP cuts PM2.5 RMSE by up to 30% and NLL by up to 73% versus standard neural processes.
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On real air-quality sensors, DnNP cuts PM2.5 RMSE by up to 30% and NLL by up to 73% versus standard neural processes.
Authors say the CPU model runs about 90 times faster than real time and leaves 11 dB less noise than Biodenoising.
The identity extends the Gaussian posterior-mean relation to kernel scoring rules and user-chosen noise paths.
Authors report a 1.6x speedup versus direct high-resolution generation and an 11.18-point drop in character error rate.
Echo learns task status without frame-level progress labels and uses those signals to condition later action denoising.
ForcingBooster cuts autoregressive video diffusion steps about 50% by stopping easy patches early.
RNADynBench ships 2,585 quality-controlled 100-ns RNA molecular dynamics trajectories, plus RNADynNet, a unified model for trajectory generation and dynamics fingerprinting.
Rolling-WAM speeds robotic replanning 4.5x by spreading joint denoising across successive cycles.
Rolling-WAM speeds robot replanning 4.5x by staggering joint video-action denoising across cycles.
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