Anima Anandkumar

This work introduces PosteriorBench, a benchmark for evaluating the distributional accuracy of generative inverse solvers, and reveals substantial distribution-matching gaps across current solvers, while showing that neural operators improve resolution robustness, and guidance weights and generation noise are key to posterior-variance calibration.

Achieving Kohn-Sham DFT accuracy without the computational burden of orbital diagonalization opens the door to simulating massive electronic systems efficiently.

Diffusion models can now solve notoriously ill-posed inverse problems in carbon capture and storage, outperforming standard methods by an order of magnitude and even rivaling asymptotically exact methods like Rejection Sampling, but with better physical realism.

Forget fixed masking ratios: this new self-supervised learning approach for time-series data dynamically adjusts noise levels to extract richer, more versatile representations.