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Found 7 papers, 1 papers with code
Physics-informed active learning for accelerating quantum chemical simulations
Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL).
Policy Relevant Treatment Effects with Multidimensional Unobserved Heterogeneity
This paper provides a framework for the policy relevant treatment effects using instrumental variables.
MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows.
Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images
The TSA interpolation module aggregates temporal contexts and then adaptively samples the spatial-related features with the proposed residual spatial adaptive block.
Weak Identification in Discrete Choice Models
We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models.
Spillovers of Program Benefits with Missing Network Links
The issue of missing network links in partially observed networks is frequently neglected in empirical studies.
Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects
This paper examines the identification power of instrumental variables (IVs) for average treatment effect (ATE) in partially identified models.
