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Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
We propose a versatile framework that combines DEQ with off-the-shelf machine learning models for predicting Hamiltonians.
Reward Imputation with Sketching for Contextual Batched Bandits
Contextual batched bandit (CBB) is a setting where a batch of rewards is observed from the environment at the end of each episode, but the rewards of the non-executed actions are unobserved, resulting in partial-information feedback.
DiffHammer: Rethinking the Robustness of Diffusion-Based Adversarial Purification Kaibo Wang
Diffusion-based purification has demonstrated impressive robustness as an adversarial defense.
Class-Conditional Conformal Prediction with Many Classes
We propose a method called clustered conformal prediction that clusters together classes having "similar" conformal
Lo-Hi: Practical ML Drug Discovery Benchmark
Finding new drugs is getting harder and harder. One of the hopes of drug discovery is to use machine learning models to predict molecular properties.
A Data and Software Availability 532 A.1 Data Availability
As shown in Figure 13, we applied higher noise settings (SNR 0.005, 0.001) to the
CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM
Single particle cryo-electron microscopy (cryo-EM) is a widely used imaging technique for visualizing biomolecular complexes at near-atomic resolution.