Aditi Krishnapriyan
@ask1729 • 1,189 subscribers
Assistant Professor at UC Berkeley
Videos
1/ A grand challenge in science: the simulation-to-experiment gap Simulators model complex systems but introduce model errors; experiments give real-world data but are costly and noisy Can we leverage both? Yes! We introduce Adversarial Distribution Alignment (ADA), at #NeurIPS2026 ADA aligns generative models—initially trained on simulations (e.g., molecular dynamics with classical force fields)—to full real-world distributions (e.g., protein structures) using only partial, noisy observations (like cryo-EM images) ADA goes beyond just matching the expectation of observables: it can match full distributions, even in the presence of multiple correlated, high-dimensional observations Check out our website: And our paper: In collaboration with kai, Toby Kreiman, and Sergey Levine
Aditi Krishnapriyan27,031 просмотров • 9 дней назад
1/ Can molecular AI move past hard-coded Graph Neural Networks and embrace scalable Transformers that discover molecular structure on their own? We demonstrate that you can train a 1B parameter Transformer model without any graph priors or physical inductive biases. And surprisingly, not only can it maintain competitive performance under equal compute on the Open Molecules 2025 dataset… it’s faster than a 6M parameter equivariant GNN, and exhibits scaling laws that don’t saturate. We use this as a starting point to investigate emergent internal representations, and find that it adaptively discovers molecular structure! Check out the interactive demo on our website: And our paper: In collaboration with Toby Kreiman, Yutong Bai, Fadi, Elizabeth, and Eric Qu. Here’s a video showing how the Transformer learns distance-aware attention patterns (purple gradient) that adapt to atomic environments 👇
Aditi Krishnapriyan40,036 просмотров • 1 год назад
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